diff --git a/demand_generator/__init__.py b/demand_generator/__init__.py
deleted file mode 100644
index e6d5f4f49684d721c27aceba42f6d81f912e094b..0000000000000000000000000000000000000000
--- a/demand_generator/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .electrical_demand import *
-from .thermal_demand import *
diff --git a/demand_generator/bdew_data/selp_series.csv b/demand_generator/bdew_data/selp_series.csv
deleted file mode 100644
index db923fbbd09c143ecd85dd0704007ae2ab385e77..0000000000000000000000000000000000000000
--- a/demand_generator/bdew_data/selp_series.csv
+++ /dev/null
@@ -1,2017 +0,0 @@
-,period,weekday,h0,g0,g1,g2,g3,g4,g5,g6,l0,l1,l2
-2007-01-01 00:00:00,summer,1,0.086360,0.071560,0.023560,0.060840,0.098520,0.074480,0.067360,0.057000,0.066160,0.057680,0.069680
-2007-01-01 00:15:00,summer,1,0.076960,0.068960,0.022040,0.052960,0.098960,0.073640,0.061160,0.052000,0.063160,0.056160,0.066560
-2007-01-01 00:30:00,summer,1,0.068840,0.066280,0.021520,0.046000,0.099360,0.072840,0.054600,0.048000,0.060520,0.054880,0.063920
-2007-01-01 00:45:00,summer,1,0.062440,0.063520,0.020800,0.040120,0.099200,0.072000,0.049360,0.045000,0.058360,0.053760,0.061840
-2007-01-01 01:00:00,summer,1,0.058040,0.060960,0.020480,0.035360,0.098080,0.071000,0.046720,0.043000,0.056680,0.052600,0.060160
-2007-01-01 01:15:00,summer,1,0.055280,0.058600,0.020440,0.031720,0.096440,0.070000,0.045960,0.042000,0.055400,0.051560,0.058880
-2007-01-01 01:30:00,summer,1,0.053600,0.056600,0.020600,0.029080,0.094800,0.069080,0.045880,0.041000,0.054440,0.050760,0.057840
-2007-01-01 01:45:00,summer,1,0.052440,0.055120,0.020800,0.027400,0.093760,0.068400,0.045320,0.040000,0.053720,0.050240,0.056880
-2007-01-01 02:00:00,summer,1,0.051360,0.054200,0.020920,0.026480,0.093640,0.068000,0.043920,0.040000,0.053160,0.050080,0.055960
-2007-01-01 02:15:00,summer,1,0.050240,0.053680,0.021000,0.026160,0.094120,0.067840,0.044280,0.039000,0.052680,0.050200,0.055080
-2007-01-01 02:30:00,summer,1,0.049240,0.053440,0.020960,0.026240,0.094760,0.067720,0.050000,0.038000,0.052280,0.050280,0.054280
-2007-01-01 02:45:00,summer,1,0.048280,0.053240,0.020800,0.026400,0.094960,0.067480,0.064480,0.038000,0.051880,0.050240,0.053600
-2007-01-01 03:00:00,summer,1,0.047440,0.053080,0.020600,0.026520,0.094400,0.067000,0.089640,0.038000,0.051440,0.049840,0.053080
-2007-01-01 03:15:00,summer,1,0.046840,0.053240,0.020400,0.026560,0.093360,0.066440,0.121040,0.038000,0.050960,0.049240,0.052640
-2007-01-01 03:30:00,summer,1,0.046560,0.054080,0.020440,0.026520,0.092200,0.065960,0.152640,0.038000,0.050480,0.048560,0.052320
-2007-01-01 03:45:00,summer,1,0.046600,0.056080,0.020800,0.026400,0.091320,0.065680,0.178320,0.038000,0.050000,0.047920,0.051920
-2007-01-01 04:00:00,summer,1,0.047120,0.059360,0.021560,0.026280,0.091040,0.065800,0.193680,0.038000,0.049600,0.047440,0.051520
-2007-01-01 04:15:00,summer,1,0.048000,0.063360,0.022400,0.026280,0.091080,0.066120,0.200560,0.037000,0.049360,0.047240,0.051280
-2007-01-01 04:30:00,summer,1,0.049240,0.067200,0.022920,0.026600,0.091120,0.066440,0.202360,0.037000,0.049480,0.047360,0.051320
-2007-01-01 04:45:00,summer,1,0.050760,0.070080,0.022680,0.027400,0.090720,0.066600,0.202520,0.037000,0.050000,0.047920,0.051920
-2007-01-01 05:00:00,summer,1,0.052640,0.071400,0.021600,0.028880,0.089760,0.066360,0.203760,0.038000,0.051160,0.048920,0.053240
-2007-01-01 05:15:00,summer,1,0.055640,0.071560,0.020680,0.031200,0.088960,0.065880,0.206040,0.039000,0.052920,0.050680,0.055200
-2007-01-01 05:30:00,summer,1,0.060520,0.071200,0.021240,0.034560,0.089160,0.065320,0.208560,0.040000,0.055320,0.053360,0.057800
-2007-01-01 05:45:00,summer,1,0.068240,0.071000,0.024600,0.039120,0.091320,0.064800,0.210560,0.040000,0.058360,0.057240,0.061000
-2007-01-01 06:00:00,summer,1,0.079200,0.071520,0.031920,0.044840,0.095880,0.064600,0.211480,0.041000,0.062160,0.062760,0.064800
-2007-01-01 06:15:00,summer,1,0.091960,0.073080,0.043840,0.050840,0.101640,0.065360,0.211520,0.041000,0.067440,0.071160,0.069400
-2007-01-01 06:30:00,summer,1,0.104760,0.075920,0.060760,0.056160,0.106920,0.067880,0.211080,0.042000,0.074840,0.084040,0.075200
-2007-01-01 06:45:00,summer,1,0.115680,0.080360,0.083200,0.059680,0.110080,0.072880,0.210560,0.043000,0.085200,0.102800,0.082440
-2007-01-01 07:00:00,summer,1,0.123440,0.086600,0.111280,0.060800,0.110080,0.080920,0.210280,0.045000,0.098920,0.128120,0.091320
-2007-01-01 07:15:00,summer,1,0.128560,0.095120,0.143720,0.060440,0.108400,0.091640,0.210280,0.048000,0.114960,0.156920,0.101600
-2007-01-01 07:30:00,summer,1,0.132000,0.106280,0.179040,0.059920,0.107080,0.104440,0.210400,0.053000,0.131840,0.185240,0.112800
-2007-01-01 07:45:00,summer,1,0.134840,0.120520,0.215600,0.060640,0.108240,0.118760,0.210560,0.058000,0.148200,0.209120,0.124480
-2007-01-01 08:00:00,summer,1,0.137800,0.137840,0.251680,0.063640,0.113280,0.133960,0.210760,0.064000,0.162640,0.225240,0.136160
-2007-01-01 08:15:00,summer,1,0.140720,0.156240,0.284720,0.068800,0.120840,0.148800,0.210760,0.070000,0.173880,0.232640,0.146720
-2007-01-01 08:30:00,summer,1,0.143200,0.173400,0.312080,0.075760,0.128880,0.162160,0.210400,0.075000,0.180600,0.230880,0.155040
-2007-01-01 08:45:00,summer,1,0.144840,0.186880,0.330960,0.084120,0.135480,0.172760,0.209560,0.080000,0.181560,0.219640,0.159920
-2007-01-01 09:00:00,summer,1,0.145320,0.194960,0.339720,0.093480,0.139080,0.179760,0.208040,0.083000,0.176080,0.199400,0.160600
-2007-01-01 09:15:00,summer,1,0.144880,0.198680,0.340880,0.103160,0.140320,0.183920,0.205760,0.085000,0.166120,0.174080,0.157840
-2007-01-01 09:30:00,summer,1,0.143760,0.199800,0.338000,0.112360,0.140360,0.186320,0.202600,0.088000,0.154120,0.148440,0.152840
-2007-01-01 09:45:00,summer,1,0.142320,0.199960,0.334720,0.120320,0.140320,0.188080,0.198480,0.091000,0.142640,0.127360,0.146720
-2007-01-01 10:00:00,summer,1,0.140800,0.200600,0.333760,0.126480,0.141080,0.190040,0.193400,0.097000,0.133800,0.114400,0.140760
-2007-01-01 10:15:00,summer,1,0.139480,0.201840,0.334640,0.131040,0.142440,0.192160,0.187800,0.103000,0.128160,0.108720,0.135960
-2007-01-01 10:30:00,summer,1,0.138520,0.203560,0.336040,0.134400,0.143960,0.194000,0.182280,0.112000,0.125800,0.108280,0.133440
-2007-01-01 10:45:00,summer,1,0.138160,0.205560,0.336640,0.136960,0.145160,0.195280,0.177320,0.121000,0.126920,0.111000,0.134360
-2007-01-01 11:00:00,summer,1,0.138640,0.207600,0.335280,0.138960,0.145680,0.195640,0.173320,0.131000,0.131160,0.114960,0.139120
-2007-01-01 11:15:00,summer,1,0.140080,0.209120,0.331760,0.140280,0.145760,0.195280,0.169680,0.141000,0.136600,0.118760,0.145600
-2007-01-01 11:30:00,summer,1,0.142640,0.209600,0.325920,0.140680,0.145880,0.194480,0.165560,0.150000,0.140920,0.121000,0.151000
-2007-01-01 11:45:00,summer,1,0.146480,0.208360,0.317720,0.139880,0.146360,0.193480,0.160200,0.156000,0.141720,0.120320,0.152520
-2007-01-01 12:00:00,summer,1,0.151520,0.205040,0.307120,0.137760,0.147440,0.192320,0.153000,0.159000,0.137400,0.115960,0.148240
-2007-01-01 12:15:00,summer,1,0.156720,0.200000,0.294440,0.134640,0.148600,0.190280,0.144640,0.159000,0.129400,0.108960,0.139760
-2007-01-01 12:30:00,summer,1,0.160760,0.193760,0.280200,0.130960,0.149080,0.186480,0.136000,0.158000,0.119920,0.100920,0.129640
-2007-01-01 12:45:00,summer,1,0.162320,0.186880,0.264760,0.127160,0.148160,0.179960,0.127960,0.155000,0.111160,0.093480,0.120360
-2007-01-01 13:00:00,summer,1,0.160480,0.179880,0.249000,0.123680,0.145400,0.170360,0.121320,0.151000,0.104840,0.087880,0.113880
-2007-01-01 13:15:00,summer,1,0.156080,0.173400,0.235240,0.120880,0.141440,0.159320,0.116560,0.147000,0.100840,0.084200,0.109880
-2007-01-01 13:30:00,summer,1,0.150200,0.168040,0.226360,0.119000,0.137200,0.148880,0.113960,0.142000,0.098520,0.082240,0.107680
-2007-01-01 13:45:00,summer,1,0.144000,0.164440,0.225040,0.118360,0.133640,0.141280,0.113840,0.135000,0.097240,0.081800,0.106360
-2007-01-01 14:00:00,summer,1,0.138440,0.163040,0.232720,0.119200,0.131480,0.138120,0.116280,0.127000,0.096560,0.082520,0.105280
-2007-01-01 14:15:00,summer,1,0.133600,0.163480,0.245280,0.121200,0.130640,0.139160,0.120080,0.118000,0.096200,0.083960,0.104480
-2007-01-01 14:30:00,summer,1,0.129360,0.165360,0.257160,0.123960,0.130880,0.143760,0.123800,0.109000,0.096160,0.085440,0.103960
-2007-01-01 14:45:00,summer,1,0.125680,0.168200,0.262880,0.127160,0.131840,0.151160,0.125960,0.103000,0.096320,0.086440,0.103880
-2007-01-01 15:00:00,summer,1,0.122440,0.171440,0.258360,0.130400,0.133320,0.160600,0.125480,0.099000,0.096640,0.086520,0.104240
-2007-01-01 15:15:00,summer,1,0.119640,0.174640,0.245680,0.133400,0.135000,0.170640,0.123000,0.097000,0.097000,0.085880,0.104880
-2007-01-01 15:30:00,summer,1,0.117400,0.177160,0.228360,0.136000,0.136640,0.179720,0.119480,0.097000,0.097240,0.084960,0.105400
-2007-01-01 15:45:00,summer,1,0.115680,0.178480,0.209920,0.137920,0.137880,0.186280,0.115880,0.098000,0.097240,0.084120,0.105520
-2007-01-01 16:00:00,summer,1,0.114640,0.178200,0.193120,0.139160,0.138600,0.189240,0.112960,0.099000,0.097000,0.083640,0.105120
-2007-01-01 16:15:00,summer,1,0.114240,0.176840,0.177600,0.140360,0.138960,0.189480,0.110880,0.101000,0.096840,0.083440,0.104680
-2007-01-01 16:30:00,summer,1,0.114600,0.175120,0.162160,0.142280,0.139240,0.188320,0.109560,0.103000,0.097080,0.083280,0.104840
-2007-01-01 16:45:00,summer,1,0.115680,0.173800,0.145600,0.145760,0.139720,0.187160,0.108800,0.106000,0.098200,0.082960,0.106360
-2007-01-01 17:00:00,summer,1,0.117600,0.173200,0.127320,0.151200,0.140560,0.186960,0.108560,0.111000,0.100560,0.082800,0.109600
-2007-01-01 17:15:00,summer,1,0.120320,0.172400,0.108440,0.157880,0.141560,0.187160,0.108200,0.116000,0.104800,0.085720,0.114320
-2007-01-01 17:30:00,summer,1,0.123880,0.170200,0.090640,0.164560,0.142400,0.186920,0.107160,0.121000,0.111520,0.095160,0.120000
-2007-01-01 17:45:00,summer,1,0.128160,0.165400,0.075640,0.170200,0.142720,0.185360,0.104800,0.127000,0.121360,0.114480,0.126120
-2007-01-01 18:00:00,summer,1,0.133240,0.157080,0.064640,0.173920,0.142280,0.181720,0.100680,0.132000,0.134400,0.145280,0.132280
-2007-01-01 18:15:00,summer,1,0.138920,0.146120,0.056960,0.176000,0.141280,0.175680,0.095240,0.137000,0.148920,0.181120,0.138360
-2007-01-01 18:30:00,summer,1,0.145040,0.133840,0.051520,0.177160,0.139920,0.167080,0.089040,0.141000,0.162640,0.213760,0.144280
-2007-01-01 18:45:00,summer,1,0.151480,0.121480,0.047280,0.178040,0.138480,0.155680,0.082600,0.146000,0.173200,0.234840,0.150040
-2007-01-01 19:00:00,summer,1,0.157960,0.110120,0.043320,0.179080,0.137160,0.141720,0.076560,0.149000,0.178920,0.238360,0.155400
-2007-01-01 19:15:00,summer,1,0.163800,0.100560,0.039640,0.180200,0.135840,0.127160,0.071160,0.152000,0.179800,0.227640,0.159520
-2007-01-01 19:30:00,summer,1,0.168280,0.093240,0.036480,0.181240,0.134360,0.114240,0.066720,0.154000,0.176480,0.208240,0.161400
-2007-01-01 19:45:00,summer,1,0.170640,0.088760,0.034040,0.181960,0.132440,0.105280,0.063480,0.156000,0.169520,0.185760,0.159920
-2007-01-01 20:00:00,summer,1,0.170400,0.087400,0.032480,0.182120,0.130040,0.101840,0.061600,0.156000,0.159600,0.164880,0.154600
-2007-01-01 20:15:00,summer,1,0.168320,0.088120,0.031560,0.181640,0.127240,0.102280,0.060640,0.156000,0.148080,0.146480,0.146600
-2007-01-01 20:30:00,summer,1,0.165280,0.089600,0.030920,0.180320,0.124160,0.104200,0.060080,0.155000,0.136400,0.130480,0.137640
-2007-01-01 20:45:00,summer,1,0.162320,0.090640,0.030240,0.178040,0.120960,0.105280,0.059440,0.154000,0.125960,0.116840,0.129440
-2007-01-01 21:00:00,summer,1,0.160120,0.090160,0.029320,0.174640,0.117800,0.103680,0.058320,0.152000,0.117920,0.105440,0.123200
-2007-01-01 21:15:00,summer,1,0.158440,0.088520,0.028240,0.169880,0.114840,0.099960,0.056960,0.150000,0.111880,0.096120,0.118640
-2007-01-01 21:30:00,summer,1,0.156840,0.086280,0.027200,0.163600,0.112240,0.095320,0.055840,0.146000,0.107080,0.088720,0.114920
-2007-01-01 21:45:00,summer,1,0.154800,0.084080,0.026480,0.155520,0.110080,0.090880,0.055400,0.140000,0.102800,0.082960,0.111280
-2007-01-01 22:00:00,summer,1,0.151920,0.082440,0.026200,0.145600,0.108480,0.087560,0.056040,0.130000,0.098480,0.078640,0.107120
-2007-01-01 22:15:00,summer,1,0.147880,0.081240,0.026200,0.134320,0.107200,0.085200,0.057640,0.119000,0.094080,0.075320,0.102480
-2007-01-01 22:30:00,summer,1,0.142560,0.080320,0.026360,0.122400,0.106000,0.083440,0.060160,0.106000,0.089600,0.072640,0.097480
-2007-01-01 22:45:00,summer,1,0.135680,0.079440,0.026480,0.110520,0.104640,0.081880,0.063480,0.095000,0.085200,0.070080,0.092320
-2007-01-01 23:00:00,summer,1,0.127200,0.078400,0.026360,0.099240,0.102880,0.080240,0.067280,0.085000,0.080960,0.067400,0.087200
-2007-01-01 23:15:00,summer,1,0.117480,0.077200,0.026000,0.088640,0.101040,0.078560,0.070600,0.076000,0.076920,0.064600,0.082240
-2007-01-01 23:30:00,summer,1,0.107080,0.075680,0.025400,0.078720,0.099480,0.076960,0.072400,0.069000,0.073080,0.061920,0.077600
-2007-01-01 23:45:00,summer,1,0.096560,0.073800,0.024600,0.069440,0.098560,0.075600,0.071520,0.062000,0.069480,0.059600,0.073360
-2007-01-02 00:00:00,summer,2,0.086360,0.071560,0.023560,0.060840,0.098520,0.074480,0.067360,0.057000,0.066160,0.057680,0.069680
-2007-01-02 00:15:00,summer,2,0.076960,0.068960,0.022040,0.052960,0.098960,0.073640,0.061160,0.052000,0.063160,0.056160,0.066560
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diff --git a/demand_generator/bdew_data/shlp_hour_factors.csv b/demand_generator/bdew_data/shlp_hour_factors.csv
deleted file mode 100644
index d1c6ffbd7dad35a022a9859997fd4fc2b59fcd4a..0000000000000000000000000000000000000000
--- a/demand_generator/bdew_data/shlp_hour_factors.csv
+++ /dev/null
@@ -1,2545 +0,0 @@
-,hour_of_day,weekday,building_class,shlp_type,temp_intervall_01,temp_intervall_02,temp_intervall_03,temp_intervall_04,temp_intervall_05,temp_intervall_06,temp_intervall_07,temp_intervall_08,temp_intervall_09,temp_intervall_10
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-1917,22,2,0,GMK,0.0348,0.0353,0.0362,0.0377,0.0356,0.0346,0.0355,0.0275,0.0276,0.0277
-1918,23,2,0,GMK,0.0326,0.0335,0.0349,0.0375,0.0342,0.0321,0.0326,0.0309,0.0305,0.0300
-1919,24,2,0,GMK,0.0353,0.0357,0.0364,0.0376,0.0349,0.0335,0.0339,0.0303,0.0301,0.0299
-1920,1,3,0,GMK,0.0377,0.0378,0.0378,0.0380,0.0358,0.0335,0.0345,0.0364,0.0364,0.0364
-1921,2,3,0,GMK,0.0395,0.0392,0.0389,0.0382,0.0356,0.0343,0.0352,0.0421,0.0430,0.0439
-1922,3,3,0,GMK,0.0366,0.0369,0.0374,0.0383,0.0367,0.0351,0.0359,0.0372,0.0375,0.0378
-1923,4,3,0,GMK,0.0416,0.0409,0.0397,0.0377,0.0365,0.0354,0.0363,0.0369,0.0375,0.0382
-1924,5,3,0,GMK,0.0421,0.0414,0.0403,0.0383,0.0362,0.0362,0.0372,0.0399,0.0414,0.0430
-1925,6,3,0,GMK,0.0397,0.0401,0.0403,0.0408,0.0390,0.0389,0.0388,0.0386,0.0395,0.0403
-1926,7,3,0,GMK,0.0540,0.0526,0.0504,0.0464,0.0503,0.0492,0.0462,0.0469,0.0454,0.0436
-1927,8,3,0,GMK,0.0619,0.0596,0.0559,0.0492,0.0562,0.0655,0.0661,0.0679,0.0718,0.0762
-1928,9,3,0,GMK,0.0617,0.0606,0.0590,0.0560,0.0590,0.0658,0.0666,0.0679,0.0738,0.0748
-1929,10,3,0,GMK,0.0583,0.0566,0.0538,0.0488,0.0551,0.0605,0.0699,0.0679,0.0651,0.0673
-1930,11,3,0,GMK,0.0528,0.0518,0.0502,0.0472,0.0513,0.0547,0.0551,0.0567,0.0601,0.0640
-1931,12,3,0,GMK,0.0566,0.0547,0.0518,0.0465,0.0480,0.0477,0.0518,0.0646,0.0653,0.0673
-1932,13,3,0,GMK,0.0425,0.0427,0.0431,0.0437,0.0431,0.0437,0.0407,0.0413,0.0407,0.0400
-1933,14,3,0,GMK,0.0344,0.0361,0.0388,0.0437,0.0429,0.0404,0.0382,0.0353,0.0331,0.0305
-1934,15,3,0,GMK,0.0350,0.0365,0.0389,0.0432,0.0448,0.0427,0.0442,0.0457,0.0463,0.0470
-1935,16,3,0,GMK,0.0305,0.0326,0.0359,0.0420,0.0427,0.0401,0.0470,0.0405,0.0436,0.0470
-1936,17,3,0,GMK,0.0328,0.0343,0.0367,0.0410,0.0418,0.0397,0.0406,0.0439,0.0442,0.0445
-1937,18,3,0,GMK,0.0341,0.0348,0.0360,0.0381,0.0353,0.0352,0.0286,0.0238,0.0200,0.0157
-1938,19,3,0,GMK,0.0351,0.0353,0.0357,0.0364,0.0347,0.0327,0.0257,0.0302,0.0256,0.0204
-1939,20,3,0,GMK,0.0340,0.0345,0.0352,0.0366,0.0348,0.0341,0.0286,0.0249,0.0215,0.0176
-1940,21,3,0,GMK,0.0364,0.0365,0.0367,0.0371,0.0355,0.0344,0.0308,0.0227,0.0200,0.0169
-1941,22,3,0,GMK,0.0348,0.0353,0.0362,0.0377,0.0356,0.0346,0.0355,0.0275,0.0276,0.0277
-1942,23,3,0,GMK,0.0326,0.0335,0.0349,0.0375,0.0342,0.0321,0.0326,0.0309,0.0305,0.0300
-1943,24,3,0,GMK,0.0353,0.0357,0.0364,0.0376,0.0349,0.0335,0.0339,0.0303,0.0301,0.0299
-1944,1,4,0,GMK,0.0377,0.0378,0.0378,0.0380,0.0358,0.0335,0.0345,0.0364,0.0364,0.0364
-1945,2,4,0,GMK,0.0395,0.0392,0.0389,0.0382,0.0356,0.0343,0.0352,0.0421,0.0430,0.0439
-1946,3,4,0,GMK,0.0366,0.0369,0.0374,0.0383,0.0367,0.0351,0.0359,0.0372,0.0375,0.0378
-1947,4,4,0,GMK,0.0416,0.0409,0.0397,0.0377,0.0365,0.0354,0.0363,0.0369,0.0375,0.0382
-1948,5,4,0,GMK,0.0421,0.0414,0.0403,0.0383,0.0362,0.0362,0.0372,0.0399,0.0414,0.0430
-1949,6,4,0,GMK,0.0397,0.0401,0.0403,0.0408,0.0390,0.0389,0.0388,0.0386,0.0395,0.0403
-1950,7,4,0,GMK,0.0540,0.0526,0.0504,0.0464,0.0503,0.0492,0.0462,0.0469,0.0454,0.0436
-1951,8,4,0,GMK,0.0619,0.0596,0.0559,0.0492,0.0562,0.0655,0.0661,0.0679,0.0718,0.0762
-1952,9,4,0,GMK,0.0617,0.0606,0.0590,0.0560,0.0590,0.0658,0.0666,0.0679,0.0738,0.0748
-1953,10,4,0,GMK,0.0583,0.0566,0.0538,0.0488,0.0551,0.0605,0.0699,0.0679,0.0651,0.0673
-1954,11,4,0,GMK,0.0528,0.0518,0.0502,0.0472,0.0513,0.0547,0.0551,0.0567,0.0601,0.0640
-1955,12,4,0,GMK,0.0566,0.0547,0.0518,0.0465,0.0480,0.0477,0.0518,0.0646,0.0653,0.0673
-1956,13,4,0,GMK,0.0425,0.0427,0.0431,0.0437,0.0431,0.0437,0.0407,0.0413,0.0407,0.0400
-1957,14,4,0,GMK,0.0344,0.0361,0.0388,0.0437,0.0429,0.0404,0.0382,0.0353,0.0331,0.0305
-1958,15,4,0,GMK,0.0350,0.0365,0.0389,0.0432,0.0448,0.0427,0.0442,0.0457,0.0463,0.0470
-1959,16,4,0,GMK,0.0305,0.0326,0.0359,0.0420,0.0427,0.0401,0.0470,0.0405,0.0436,0.0470
-1960,17,4,0,GMK,0.0328,0.0343,0.0367,0.0410,0.0418,0.0397,0.0406,0.0439,0.0442,0.0445
-1961,18,4,0,GMK,0.0341,0.0348,0.0360,0.0381,0.0353,0.0352,0.0286,0.0238,0.0200,0.0157
-1962,19,4,0,GMK,0.0351,0.0353,0.0357,0.0364,0.0347,0.0327,0.0257,0.0302,0.0256,0.0204
-1963,20,4,0,GMK,0.0340,0.0345,0.0352,0.0366,0.0348,0.0341,0.0286,0.0249,0.0215,0.0176
-1964,21,4,0,GMK,0.0364,0.0365,0.0367,0.0371,0.0355,0.0344,0.0308,0.0227,0.0200,0.0169
-1965,22,4,0,GMK,0.0348,0.0353,0.0362,0.0377,0.0356,0.0346,0.0355,0.0275,0.0276,0.0277
-1966,23,4,0,GMK,0.0326,0.0335,0.0349,0.0375,0.0342,0.0321,0.0326,0.0309,0.0305,0.0300
-1967,24,4,0,GMK,0.0353,0.0357,0.0364,0.0376,0.0349,0.0335,0.0339,0.0303,0.0301,0.0299
-1968,1,5,0,GMK,0.0308,0.0320,0.0332,0.0375,0.0356,0.0339,0.0369,0.0368,0.0383,0.0398
-1969,2,5,0,GMK,0.0329,0.0334,0.0338,0.0370,0.0347,0.0359,0.0380,0.0372,0.0381,0.0389
-1970,3,5,0,GMK,0.0326,0.0333,0.0339,0.0392,0.0352,0.0344,0.0379,0.0369,0.0375,0.0381
-1971,4,5,0,GMK,0.0323,0.0332,0.0342,0.0387,0.0360,0.0370,0.0409,0.0386,0.0394,0.0402
-1972,5,5,0,GMK,0.0344,0.0351,0.0359,0.0386,0.0374,0.0368,0.0341,0.0495,0.0472,0.0449
-1973,6,5,0,GMK,0.0317,0.0335,0.0350,0.0390,0.0382,0.0390,0.0437,0.0520,0.0585,0.0649
-1974,7,5,0,GMK,0.0643,0.0606,0.0569,0.0476,0.0495,0.0459,0.0570,0.0511,0.0538,0.0564
-1975,8,5,0,GMK,0.0718,0.0677,0.0637,0.0489,0.0555,0.0625,0.0632,0.0615,0.0610,0.0606
-1976,9,5,0,GMK,0.0675,0.0648,0.0621,0.0520,0.0567,0.0665,0.0623,0.0613,0.0629,0.0645
-1977,10,5,0,GMK,0.0669,0.0640,0.0611,0.0489,0.0552,0.0625,0.0593,0.0578,0.0587,0.0596
-1978,11,5,0,GMK,0.0546,0.0541,0.0536,0.0474,0.0527,0.0528,0.0573,0.0560,0.0572,0.0583
-1979,12,5,0,GMK,0.0463,0.0469,0.0475,0.0466,0.0487,0.0472,0.0453,0.0451,0.0441,0.0431
-1980,13,5,0,GMK,0.0420,0.0427,0.0433,0.0450,0.0447,0.0410,0.0421,0.0382,0.0361,0.0340
-1981,14,5,0,GMK,0.0461,0.0456,0.0450,0.0462,0.0440,0.0391,0.0315,0.0432,0.0453,0.0475
-1982,15,5,0,GMK,0.0496,0.0486,0.0476,0.0449,0.0456,0.0409,0.0410,0.0421,0.0427,0.0433
-1983,16,5,0,GMK,0.0416,0.0422,0.0428,0.0424,0.0440,0.0434,0.0394,0.0418,0.0410,0.0402
-1984,17,5,0,GMK,0.0448,0.0435,0.0422,0.0425,0.0396,0.0396,0.0343,0.0330,0.0297,0.0264
-1985,18,5,0,GMK,0.0262,0.0290,0.0318,0.0358,0.0374,0.0351,0.0318,0.0271,0.0236,0.0202
-1986,19,5,0,GMK,0.0303,0.0316,0.0330,0.0365,0.0357,0.0348,0.0283,0.0229,0.0186,0.0144
-1987,20,5,0,GMK,0.0280,0.0301,0.0322,0.0367,0.0365,0.0344,0.0339,0.0306,0.0286,0.0267
-1988,21,5,0,GMK,0.0290,0.0304,0.0319,0.0368,0.0348,0.0352,0.0340,0.0315,0.0304,0.0293
-1989,22,5,0,GMK,0.0306,0.0319,0.0333,0.0375,0.0359,0.0351,0.0349,0.0323,0.0311,0.0299
-1990,23,5,0,GMK,0.0323,0.0324,0.0326,0.0371,0.0329,0.0331,0.0378,0.0379,0.0396,0.0413
-1991,24,5,0,GMK,0.0334,0.0334,0.0334,0.0372,0.0335,0.0339,0.0351,0.0356,0.0366,0.0375
-1992,1,6,0,GMK,0.0411,0.0410,0.0406,0.0397,0.0388,0.0360,0.0324,0.0329,0.0325,0.0319
-1993,2,6,0,GMK,0.0422,0.0419,0.0414,0.0431,0.0390,0.0382,0.0336,0.0325,0.0318,0.0309
-1994,3,6,0,GMK,0.0414,0.0413,0.0410,0.0416,0.0396,0.0362,0.0343,0.0321,0.0311,0.0299
-1995,4,6,0,GMK,0.0417,0.0416,0.0412,0.0432,0.0394,0.0383,0.0316,0.0402,0.0426,0.0448
-1996,5,6,0,GMK,0.0447,0.0443,0.0434,0.0432,0.0395,0.0393,0.0355,0.0345,0.0332,0.0318
-1997,6,6,0,GMK,0.0424,0.0421,0.0419,0.0421,0.0402,0.0442,0.0426,0.0369,0.0345,0.0322
-1998,7,6,0,GMK,0.0569,0.0543,0.0537,0.0476,0.0507,0.0527,0.0657,0.0724,0.0757,0.0787
-1999,8,6,0,GMK,0.0507,0.0509,0.0509,0.0447,0.0510,0.0603,0.0646,0.0705,0.0780,0.0853
-2000,9,6,0,GMK,0.0446,0.0458,0.0475,0.0449,0.0553,0.0633,0.0718,0.0793,0.0829,0.0863
-2001,10,6,0,GMK,0.0503,0.0509,0.0517,0.0452,0.0553,0.0565,0.0585,0.0708,0.0804,0.0899
-2002,11,6,0,GMK,0.0391,0.0405,0.0423,0.0439,0.0510,0.0542,0.0645,0.0703,0.0808,0.0912
-2003,12,6,0,GMK,0.0422,0.0431,0.0443,0.0457,0.0502,0.0508,0.0641,0.0586,0.0645,0.0703
-2004,13,6,0,GMK,0.0383,0.0386,0.0389,0.0396,0.0404,0.0405,0.0415,0.0414,0.0432,0.0449
-2005,14,6,0,GMK,0.0394,0.0390,0.0384,0.0395,0.0353,0.0353,0.0380,0.0319,0.0313,0.0306
-2006,15,6,0,GMK,0.0347,0.0348,0.0348,0.0377,0.0347,0.0345,0.0426,0.0415,0.0378,0.0338
-2007,16,6,0,GMK,0.0396,0.0394,0.0389,0.0378,0.0367,0.0336,0.0367,0.0258,0.0227,0.0194
-2008,17,6,0,GMK,0.0389,0.0388,0.0384,0.0393,0.0367,0.0348,0.0258,0.0303,0.0290,0.0275
-2009,18,6,0,GMK,0.0376,0.0376,0.0376,0.0391,0.0375,0.0355,0.0311,0.0253,0.0174,0.0092
-2010,19,6,0,GMK,0.0367,0.0369,0.0371,0.0399,0.0379,0.0359,0.0235,0.0152,0.0054,0.0000
-2011,20,6,0,GMK,0.0399,0.0398,0.0396,0.0392,0.0385,0.0353,0.0324,0.0267,0.0190,0.0108
-2012,21,6,0,GMK,0.0335,0.0341,0.0348,0.0403,0.0384,0.0363,0.0272,0.0320,0.0286,0.0250
-2013,22,6,0,GMK,0.0424,0.0421,0.0414,0.0410,0.0384,0.0378,0.0395,0.0373,0.0383,0.0391
-2014,23,6,0,GMK,0.0394,0.0393,0.0391,0.0410,0.0381,0.0343,0.0328,0.0320,0.0319,0.0316
-2015,24,6,0,GMK,0.0423,0.0419,0.0411,0.0407,0.0374,0.0362,0.0297,0.0296,0.0274,0.0249
-2016,1,7,0,GMK,0.0408,0.0410,0.0413,0.0394,0.0419,0.0402,0.0434,0.0445,0.0483,0.0551
-2017,2,7,0,GMK,0.0472,0.0463,0.0453,0.0402,0.0432,0.0455,0.0450,0.0549,0.0627,0.0762
-2018,3,7,0,GMK,0.0465,0.0456,0.0447,0.0390,0.0427,0.0429,0.0629,0.0585,0.0684,0.0892
-2019,4,7,0,GMK,0.0499,0.0484,0.0467,0.0399,0.0429,0.0445,0.0512,0.0719,0.0856,0.1088
-2020,5,7,0,GMK,0.0398,0.0405,0.0412,0.0395,0.0427,0.0470,0.0534,0.0768,0.0937,0.1221
-2021,6,7,0,GMK,0.0395,0.0403,0.0414,0.0401,0.0436,0.0468,0.0608,0.0640,0.0782,0.1020
-2022,7,7,0,GMK,0.0429,0.0434,0.0440,0.0480,0.0452,0.0507,0.0452,0.0463,0.0471,0.0493
-2023,8,7,0,GMK,0.0453,0.0460,0.0467,0.0451,0.0484,0.0518,0.0521,0.0417,0.0440,0.0486
-2024,9,7,0,GMK,0.0464,0.0464,0.0465,0.0462,0.0465,0.0510,0.0468,0.0349,0.0333,0.0315
-2025,10,7,0,GMK,0.0396,0.0408,0.0420,0.0445,0.0449,0.0454,0.0396,0.0454,0.0414,0.0361
-2026,11,7,0,GMK,0.0377,0.0386,0.0396,0.0420,0.0417,0.0431,0.0374,0.0435,0.0406,0.0370
-2027,12,7,0,GMK,0.0458,0.0447,0.0435,0.0414,0.0409,0.0403,0.0341,0.0371,0.0309,0.0218
-2028,13,7,0,GMK,0.0344,0.0358,0.0373,0.0399,0.0407,0.0375,0.0330,0.0350,0.0276,0.0167
-2029,14,7,0,GMK,0.0364,0.0368,0.0372,0.0400,0.0381,0.0349,0.0288,0.0282,0.0185,0.0038
-2030,15,7,0,GMK,0.0449,0.0435,0.0420,0.0404,0.0386,0.0352,0.0293,0.0289,0.0193,0.0047
-2031,16,7,0,GMK,0.0322,0.0338,0.0355,0.0408,0.0393,0.0374,0.0263,0.0218,0.0073,0.0000
-2032,17,7,0,GMK,0.0341,0.0349,0.0358,0.0418,0.0377,0.0353,0.0325,0.0219,0.0145,0.0032
-2033,18,7,0,GMK,0.0448,0.0434,0.0418,0.0406,0.0383,0.0355,0.0315,0.0340,0.0277,0.0185
-2034,19,7,0,GMK,0.0463,0.0446,0.0427,0.0409,0.0386,0.0367,0.0307,0.0319,0.0240,0.0123
-2035,20,7,0,GMK,0.0388,0.0390,0.0391,0.0409,0.0394,0.0384,0.0379,0.0141,0.0418,0.0248
-2036,21,7,0,GMK,0.0398,0.0400,0.0401,0.0420,0.0404,0.0392,0.0412,0.0389,0.0199,0.0151
-2037,22,7,0,GMK,0.0382,0.0389,0.0397,0.0427,0.0416,0.0406,0.0476,0.0530,0.0516,0.0433
-2038,23,7,0,GMK,0.0503,0.0483,0.0462,0.0424,0.0416,0.0407,0.0437,0.0422,0.0409,0.0432
-2039,24,7,0,GMK,0.0384,0.0390,0.0397,0.0423,0.0411,0.0394,0.0456,0.0306,0.0327,0.0367
-2040,1,1,0,GPD,0.0392,0.0393,0.0394,0.0395,0.0391,0.0382,0.0355,0.0416,0.0438,0.0441
-2041,2,1,0,GPD,0.0396,0.0398,0.0400,0.0401,0.0410,0.0398,0.0354,0.0416,0.0437,0.0439
-2042,3,1,0,GPD,0.0403,0.0403,0.0403,0.0403,0.0411,0.0396,0.0361,0.0416,0.0436,0.0438
-2043,4,1,0,GPD,0.0386,0.0384,0.0381,0.0401,0.0410,0.0403,0.0379,0.0426,0.0446,0.0448
-2044,5,1,0,GPD,0.0399,0.0400,0.0401,0.0398,0.0415,0.0429,0.0447,0.0497,0.0526,0.0533
-2045,6,1,0,GPD,0.0405,0.0404,0.0407,0.0412,0.0439,0.0473,0.0464,0.0517,0.0540,0.0548
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diff --git a/demand_generator/bdew_data/shlp_sigmoid_factors.csv b/demand_generator/bdew_data/shlp_sigmoid_factors.csv
deleted file mode 100644
index 8acbc8d13f09eb6ec9ebb5e71c3b785d59231ab9..0000000000000000000000000000000000000000
--- a/demand_generator/bdew_data/shlp_sigmoid_factors.csv
+++ /dev/null
@@ -1,69 +0,0 @@
-,parameter_a,parameter_b,parameter_c,parameter_d,building_class,shlp_type,wind_impact
-0,3.2279446000,-37.4214800000,6.2222288000,0.0828441000,1,EFH,1
-1,3.2107659000,-37.4178801000,6.2024000000,0.0865217000,2,EFH,1
-2,3.1935978000,-37.4142478000,6.1824021000,0.0901957000,3,EFH,1
-3,3.1764404000,-37.4105832000,6.1622336000,0.0938662000,4,EFH,1
-4,3.1592940000,-37.4068860000,6.1418926000,0.0975329000,5,EFH,1
-5,3.1421588000,-37.4031563000,6.1213773000,0.1011959000,6,EFH,1
-6,3.1250349000,-37.3993941000,6.1006859000,0.1048550000,7,EFH,1
-7,3.1079226000,-37.3955994000,6.0798166000,0.1085103000,8,EFH,1
-8,3.0908222000,-37.3917722000,6.0587677000,0.1121617000,9,EFH,1
-9,3.0737337000,-37.3879125000,6.0375374000,0.1158091000,10,EFH,1
-10,3.1850191000,-37.4124155000,6.1723179000,0.0920309000,11,EFH,1
-11,3.0890721000,-37.1849497000,5.7137959000,0.1071295000,1,EFH,0
-12,3.0722215000,-37.1842844000,5.6975234000,0.1108074000,2,EFH,0
-13,3.0553842000,-37.1836374000,5.6810825000,0.1144800000,3,EFH,0
-14,3.0385547000,-37.1829908000,5.6644869000,0.1181514000,4,EFH,0
-15,3.0217399000,-37.1823600000,5.6477170000,0.1218169000,5,EFH,0
-16,3.0049303000,-37.1817595000,5.6308191000,0.1254825000,6,EFH,0
-17,2.9881355000,-37.1811865000,5.6137482000,0.1291431000,7,EFH,0
-18,2.9713479000,-37.1806299000,5.5965242000,0.1328015000,8,EFH,0
-19,2.9545709000,-37.1800956000,5.5791367000,0.1364576000,9,EFH,0
-20,2.9378047000,-37.1795853000,5.5615838000,0.1401107000,10,EFH,0
-21,3.0469695000,-37.1833141000,5.6727847000,0.1163157000,11,EFH,0
-22,2.5736652000,-35.0169442000,6.1318140000,0.0996851000,1,MFH,1
-23,2.5516882000,-35.0234219000,6.1680699000,0.1087080000,2,MFH,1
-24,2.5297380000,-35.0300145000,6.2051109000,0.1177216000,3,MFH,1
-25,2.5078170000,-35.0367363000,6.2430159000,0.1267238000,4,MFH,1
-26,2.4859161000,-35.0435978000,6.2818214000,0.1357193000,5,MFH,1
-27,2.4640414000,-35.0505874000,6.3215140000,0.1447056000,6,MFH,1
-28,2.4421941000,-35.0577078000,6.3621285000,0.1536820000,7,MFH,1
-29,2.4203748000,-35.0649619000,6.4036973000,0.1626484000,8,MFH,1
-30,2.3985840000,-35.0723524000,6.4462530000,0.1716044000,9,MFH,1
-31,2.3768224000,-35.0798821000,6.4898289000,0.1805499000,10,MFH,1
-32,2.5187775000,-35.0333754000,6.2240634000,0.1222227000,11,MFH,1
-33,2.4428072000,-34.7321438000,5.7347347000,0.1236492000,1,MFH,0
-34,2.4207684000,-34.7277917000,5.7668252000,0.1326318000,2,MFH,0
-35,2.3987552000,-34.7234878000,5.7996446000,0.1416084000,3,MFH,0
-36,2.3767684000,-34.7192333000,5.8332162000,0.1505787000,4,MFH,0
-37,2.3548083000,-34.7150299000,5.8675639000,0.1595427000,5,MFH,0
-38,2.3328756000,-34.7108776000,5.9027152000,0.1685000000,6,MFH,0
-39,2.3109709000,-34.7067802000,5.9386943000,0.1774504000,7,MFH,0
-40,2.2890947000,-34.7027395000,5.9755295000,0.1863939000,8,MFH,0
-41,2.2672475000,-34.6987574000,6.0132489000,0.1953302000,9,MFH,0
-42,2.2454319000,-34.6948549000,6.0518709000,0.2042586000,10,MFH,0
-43,2.3877618000,-34.7213605000,5.8164304000,0.1460936000,11,MFH,0
-44,3.1177248000,-35.8715062000,7.5186829000,0.0343301000,0,GMK,1
-45,2.7882424000,-34.8806130000,6.5951899000,0.0540329000,0,GMK,0
-46,3.8500000000,-37.0000000000,10.2405021000,0.0469243000,0,GPD,1
-47,2.5784173000,-34.7321261000,6.4805035000,0.1407729000,0,GPD,0
-48,4.0196902000,-37.8282037000,8.1593369000,0.0472845000,0,GHA,1
-49,3.5811214000,-36.9650065000,7.2256947000,0.0448416000,0,GHA,0
-50,3.7500000000,-37.5000000000,6.8000000000,0.0609113000,0,GBD,1
-51,2.9177027000,-36.1794117000,5.9265162000,0.1151912000,0,GBD,0
-52,3.4428943000,-36.6590504000,7.6083226000,0.0746850000,0,GKO,1
-53,2.7172288000,-35.1412563000,7.1303395000,0.1418472000,0,GKO,0
-54,2.4595181000,-35.2532123000,6.0587001000,0.1647370000,0,GBH,1
-55,2.0102472000,-35.2532123000,6.1544406000,0.3294741000,0,GBH,0
-56,2.8195656000,-36.0000000000,7.7368518000,0.1572810000,0,GGA,1
-57,2.2850165000,-36.2878584000,6.5885126000,0.3150535000,0,GGA,0
-58,0.9315889000,-33.3500000000,5.7212303000,0.6656494000,0,GBA,1
-59,0.6522601000,-37.1729781000,5.5973647000,0.8220629000,0,GBA,0
-60,1.0535875000,-35.3000000000,4.8662747000,0.6811042000,0,GWA,1
-61,0.7657290000,-36.0237911000,4.8662747000,0.8049425000,0,GWA,0
-62,3.6017736000,-37.8825368000,6.9836070000,0.0548262000,0,GGB,1
-63,3.3904645000,-39.2875216000,4.4905740000,0.0834783000,0,GGB,0
-64,2.5187775000,-35.0333754000,6.2240634000,0.0977782000,0,GMF,1
-65,2.3877618000,-34.7213605000,5.8164304000,0.1168748000,0,GMF,0
-66,2.5792510140,-35.6816144000,6.6857976120,0.1995540990,0,GHD,1
-67,3.0084345560,-36.6078452700,7.3211869530,0.1549660310,0,GHD,0
diff --git a/demand_generator/bdew_data/shlp_weekday_factors.csv b/demand_generator/bdew_data/shlp_weekday_factors.csv
deleted file mode 100644
index c3cbd9df8b5533ae437a9881361820d5308cd4cd..0000000000000000000000000000000000000000
--- a/demand_generator/bdew_data/shlp_weekday_factors.csv
+++ /dev/null
@@ -1,99 +0,0 @@
-,shlp_type,wochentagsfaktor
-0,EFH,1.000
-1,EFH,1.000
-2,EFH,1.000
-3,EFH,1.000
-4,EFH,1.000
-5,EFH,1.000
-6,EFH,1.000
-7,MFH,1.000
-8,MFH,1.000
-9,MFH,1.000
-10,MFH,1.000
-11,MFH,1.000
-12,MFH,1.000
-13,MFH,1.000
-14,GMK,1.070
-15,GMK,1.037
-16,GMK,0.993
-17,GMK,0.995
-18,GMK,1.066
-19,GMK,0.936
-20,GMK,0.903
-21,GPD,1.021
-22,GPD,1.087
-23,GPD,1.072
-24,GPD,1.056
-25,GPD,1.012
-26,GPD,0.900
-27,GPD,0.851
-28,GHA,1.036
-29,GHA,1.023
-30,GHA,1.025
-31,GHA,1.030
-32,GHA,1.025
-33,GHA,0.967
-34,GHA,0.893
-35,GBD,1.105
-36,GBD,1.086
-37,GBD,1.038
-38,GBD,1.062
-39,GBD,1.027
-40,GBD,0.763
-41,GBD,0.898
-42,GKO,1.035
-43,GKO,1.052
-44,GKO,1.045
-45,GKO,1.049
-46,GKO,0.988
-47,GKO,0.886
-48,GKO,0.944
-49,GBH,0.977
-50,GBH,1.039
-51,GBH,1.003
-52,GBH,1.016
-53,GBH,1.002
-54,GBH,1.004
-55,GBH,0.958
-56,GGA,0.932
-57,GGA,0.989
-58,GGA,1.003
-59,GGA,1.011
-60,GGA,1.018
-61,GGA,1.036
-62,GGA,1.009
-63,GBA,1.085
-64,GBA,1.121
-65,GBA,1.077
-66,GBA,1.135
-67,GBA,1.140
-68,GBA,0.485
-69,GBA,0.958
-70,GWA,1.246
-71,GWA,1.261
-72,GWA,1.271
-73,GWA,1.243
-74,GWA,1.128
-75,GWA,0.388
-76,GWA,0.462
-77,GGB,0.990
-78,GGB,0.963
-79,GGB,1.051
-80,GGB,1.055
-81,GGB,1.030
-82,GGB,0.977
-83,GGB,0.936
-84,GMF,1.035
-85,GMF,1.052
-86,GMF,1.045
-87,GMF,1.049
-88,GMF,0.988
-89,GMF,0.886
-90,GMF,0.944
-91,GHD,1.030
-92,GHD,1.030
-93,GHD,1.020
-94,GHD,1.030
-95,GHD,1.010
-96,GHD,0.930
-97,GHD,0.940
diff --git a/demand_generator/bdew_data/smooth.pkl b/demand_generator/bdew_data/smooth.pkl
deleted file mode 100644
index 489a50da3f7bb774fbd68e459150112959386aa6..0000000000000000000000000000000000000000
Binary files a/demand_generator/bdew_data/smooth.pkl and /dev/null differ
diff --git a/demand_generator/electrical_demand/ElectricalDemand.py b/demand_generator/electrical_demand/ElectricalDemand.py
deleted file mode 100644
index 4a5ea5d5e203e5b1f6d216f41db11ec99a6db3dc..0000000000000000000000000000000000000000
--- a/demand_generator/electrical_demand/ElectricalDemand.py
+++ /dev/null
@@ -1,201 +0,0 @@
-"""
-MIT License
-
-Copyright (c) 2023 RWTH Aachen University
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
-"""
-# Based on demandlib by oemof and Repraesentative Lastprofile (VDEW 1999)
-# Smoothing factors pkl for household demand calculated with formula from VDEW 1999
-# Formula adjusted for accuracy by oemof: https://github.com/oemof/demandlib/blob/master/demandlib/bdew.py
-
-import calendar
-import datetime
-import numpy as np
-import os
-import pandas as pd
-
-
-def add_weekdays2df(time_df, holidays=None, holiday_is_sunday=False):
-    r"""Giving back a DataFrame containing weekdays and optionally holidays for
-     the given year.
-    Parameters
-    ----------
-    time_df : pandas DataFrame
-        DataFrame to which the weekdays should be added
-    Optional Parameters
-    -------------------
-    holidays : array with information for every hour of the year, if holiday or
-        not (0: holiday, 1: no holiday)
-    holiday_is_sunday : boolean
-        If set to True, all holidays (0) will be set to sundays (7).
-    Returns
-    -------
-    pandas.DataFrame : DataFrame with weekdays
-    Notes
-    -----
-    Using Pandas > 0.16
-    """
-    time_df['weekday'] = time_df.index.weekday + 1
-    time_df['date'] = time_df.index.date
-
-    # Set weekday to Holiday (0) for all holidays
-    if holidays is not None:
-        if isinstance(holidays, dict):
-            holidays = list(holidays.keys())
-        time_df['weekday'].mask(pd.to_datetime(time_df['date']).isin(
-            pd.to_datetime(holidays)), 0, True)
-
-    if holiday_is_sunday:
-        time_df.weekday.mask(time_df.weekday == 0, 7, True)
-
-    return time_df
-
-
-class ElectricalDemand:
-    """Generate electrical standardized load profiles based on the BDEW method.
-    Attributes
-    ----------
-    datapath : string
-        Path to the csv files containing the load profile data.
-    date_time_index : pandas.DateTimeIndex
-        Time range for and frequency for the profile.
-    Parameters
-    ----------
-    year : integer
-        Year of the demand series.
-    Optional Parameters
-    -------------------
-    seasons : dictionary
-        Describing the time ranges for summer, winter and transition periods.
-    holidays : dictionary or list
-        The keys of the dictionary or the items of the list should be datetime
-        objects of the days that are holidays.
-    """
-
-    def __init__(self, year, seasons=None, holidays=None):
-        if calendar.isleap(year):
-            hoy = 8784
-        else:
-            hoy = 8760
-        file_dir = os.path.dirname(os.path.abspath(__file__))
-        for j in range(1):
-            file_dir = os.path.dirname(file_dir)
-        self.datapath = os.path.join(file_dir, 'bdew_data')
-        self.date_time_index = pd.date_range(
-            datetime.datetime(year, 1, 1, 0), periods=hoy * 4, freq='15Min')
-        if seasons is None:
-            self.seasons = {
-                'summer1': [5, 15, 9, 14],  # summer: 15.05. to 14.09
-                'transition1': [3, 21, 5, 14],  # transition1 :21.03. to 14.05
-                'transition2': [9, 15, 10, 31],  # transition2 :15.09. to 31.10
-                'winter1': [1, 1, 3, 20],  # winter1:  01.01. to 20.03
-                'winter2': [11, 1, 12, 31],  # winter2: 01.11. to 31.12
-            }
-        else:
-            self.seasons = seasons
-        self.year = year
-        self.slp_frame = self.all_load_profiles(self.date_time_index,
-                                                holidays=holidays)
-
-    def all_load_profiles(self, time_df, holidays=None):
-        slp_types = ['h0', 'g0', 'g1', 'g2', 'g3', 'g4', 'g5', 'g6', 'l0',
-                     'l1', 'l2']
-        new_df = self.create_bdew_load_profiles(time_df, slp_types,
-                                                holidays=holidays)
-
-        new_df.drop(['hour', 'weekday'], 1, inplace=True)
-        return new_df
-
-    def create_bdew_load_profiles(self, dt_index, slp_types, holidays=None):
-        """Calculates the hourly electricity load profile in MWh/h of a region.
-        """
-
-        # define file path of slp csv data
-        file_path = os.path.join(self.datapath, 'selp_series.csv')
-
-        # Read standard load profile series from csv file
-        selp_series = pd.read_csv(file_path)
-        tmp_df = selp_series
-        # Create an index to merge. The year and month will be ignored only the
-        # time index is necessary.
-        index = pd.date_range(
-            datetime.datetime(2007, 1, 1, 0), periods=2016, freq='15Min')
-        tmp_df.set_index(index, inplace=True)
-
-        # Create empty DataFrame to take the results.
-        new_df = pd.DataFrame(index=dt_index, columns=slp_types).fillna(0)
-        new_df = add_weekdays2df(new_df, holidays=holidays,
-                                 holiday_is_sunday=True)
-
-        new_df['hour'] = dt_index.hour + 1
-        new_df['minute'] = dt_index.minute
-        time_df = new_df[['date', 'hour', 'minute', 'weekday']].copy()
-        tmp_df[slp_types] = tmp_df[slp_types].astype(float)
-
-        # Inner join the slps on the time_df to the slp's for a whole year
-        tmp_df['hour_of_day'] = tmp_df.index.hour + 1
-        tmp_df['minute_of_hour'] = tmp_df.index.minute
-        left_cols = ['hour_of_day', 'minute_of_hour', 'weekday']
-        right_cols = ['hour', 'minute', 'weekday']
-        tmp_df = tmp_df.reset_index()
-        tmp_df.pop('index')
-
-        for p in self.seasons.keys():
-            a = datetime.datetime(self.year, self.seasons[p][0],
-                                  self.seasons[p][1], 0, 0)
-            b = datetime.datetime(self.year, self.seasons[p][2],
-                                  self.seasons[p][3], 23, 59)
-
-            new_df.update(pd.DataFrame.merge(
-                tmp_df[tmp_df['period'] == p[:-1]], time_df[a:b],
-                left_on=left_cols, right_on=right_cols, how='inner').sort_values(
-                by=["date", "weekday", "hour", "minute"]).set_index(
-                time_df[a:b].index).drop(
-                ['hour_of_day'], 1))
-
-        new_df.drop('date', axis=1, inplace=True)
-        return new_df.div(new_df.sum(axis=0), axis=1)
-
-    def get_profile(self, ann_demand, slp_type,
-                    dyn_function_h0: bool = True):
-        """ Get the profiles for the given annual demand
-        Parameters
-        ----------
-        :param ann_demand : number
-            annual demand for the prosumer
-        :param dyn_function_h0: bool, default True
-            Uses the dynamization function of the BDEW to smoothen the
-            seasonal edges. Functions resolution is daily.
-            f(x) = -3.916649251 * 10^-10 * x^4 + 3,2 * 10^-7 * x³ - 7,02
-            * 10^-5 * x²+0,0021 * x +1,24
-            Adjustment of accuracy: from -3,92 to -3.916649251
-        Returns
-        -------
-        pandas.Series : Selected profile in time steps of 15 minutes
-        :param slp_type: Indicates consumer type
-        """
-        if dyn_function_h0:
-            quartersinyear = len(self.slp_frame)
-            # vectorization of the smoothing proposed by oemof in order to save computation time (the dynamization
-            # function presented above was resolved outside of the project since it is not dependant on any parameters)
-            self.slp_frame['h0'] = \
-                np.multiply(np.array(self.slp_frame['h0']),
-                            np.array(pd.read_pickle(os.path.join(self.datapath, 'smooth.pkl'))[:quartersinyear]))
-        return self.slp_frame[slp_type]*ann_demand*4
diff --git a/demand_generator/electrical_demand/__init__.py b/demand_generator/electrical_demand/__init__.py
deleted file mode 100644
index f13315f0c2467f1b6e1ef98191125ad12c99714b..0000000000000000000000000000000000000000
--- a/demand_generator/electrical_demand/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .ElectricalDemand import ElectricalDemand
diff --git a/demand_generator/tek_data/DHW_profile.xlsx b/demand_generator/tek_data/DHW_profile.xlsx
deleted file mode 100644
index 2ac34f53785b47511fd18be55016d3fa31eb5a43..0000000000000000000000000000000000000000
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diff --git a/demand_generator/tek_data/GHD_Zonierung.xlsx b/demand_generator/tek_data/GHD_Zonierung.xlsx
deleted file mode 100644
index 8277d2d07ae6deb550e43224a9cada936f4aa3fa..0000000000000000000000000000000000000000
Binary files a/demand_generator/tek_data/GHD_Zonierung.xlsx and /dev/null differ
diff --git a/demand_generator/tek_data/GHD_profile.xlsx b/demand_generator/tek_data/GHD_profile.xlsx
deleted file mode 100644
index 374862d905f41ec7949db65776a707a36b13b72a..0000000000000000000000000000000000000000
Binary files a/demand_generator/tek_data/GHD_profile.xlsx and /dev/null differ
diff --git "a/demand_generator/tek_data/IWU_Standardzonierungen f\303\274r die Hauptkategorien von NWG in Anlehnung an BWZK_v02.xlsx" "b/demand_generator/tek_data/IWU_Standardzonierungen f\303\274r die Hauptkategorien von NWG in Anlehnung an BWZK_v02.xlsx"
deleted file mode 100644
index ac7466897f52f1a9df7cad54bd8c84264f6f5c59..0000000000000000000000000000000000000000
Binary files "a/demand_generator/tek_data/IWU_Standardzonierungen f\303\274r die Hauptkategorien von NWG in Anlehnung an BWZK_v02.xlsx" and /dev/null differ
diff --git a/demand_generator/tek_data/TABULA_data.xlsx b/demand_generator/tek_data/TABULA_data.xlsx
deleted file mode 100644
index 4c8fff27b0d64059cb14e4eeea539b84ed147519..0000000000000000000000000000000000000000
Binary files a/demand_generator/tek_data/TABULA_data.xlsx and /dev/null differ
diff --git a/demand_generator/tek_data/TEK_Teilenergiekennwerte.xlsx b/demand_generator/tek_data/TEK_Teilenergiekennwerte.xlsx
deleted file mode 100644
index 00ea4697711a73b995ea2ed6624ef043474f4f57..0000000000000000000000000000000000000000
Binary files a/demand_generator/tek_data/TEK_Teilenergiekennwerte.xlsx and /dev/null differ
diff --git a/demand_generator/tek_data/gen_heat_profile.py b/demand_generator/tek_data/gen_heat_profile.py
deleted file mode 100644
index fac80db9718ba28b59424337e155774e12242c65..0000000000000000000000000000000000000000
--- a/demand_generator/tek_data/gen_heat_profile.py
+++ /dev/null
@@ -1,426 +0,0 @@
-"""
-MIT License
-
-Copyright (c) 2023 RWTH Aachen University
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
-"""
-"""
-This tool use the TEK project of IWU to calculate the annual energy demand of
-the building and use the degree day method to generate the demand profile.
-"""
-
-import os
-import datetime
-from warnings import warn
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-
-# ==============================================================================
-#                     Non-residential Building List
-# ==============================================================================
-
-building_typ_list = ["Verwaltungsgebäude",
-                     "Büro und Dienstleistungsgebäude",
-                     "Hochschule und Forschung",
-                     "Gesundheitswesen",
-                     "Bildungseinrichtungen",
-                     "Kultureinrichtungen",
-                     "Sporteinrichtungen",
-                     "Beherbergen und Verpflegen",
-                     "Gewerbliche und industrielle",
-                     "Verkaufsstätten",
-                     "Technikgebäude",
-                     "Wohngebäude",
-                     "Wohngebäude (MFH)"]
-energy_typ_list = ["sehr hoch", "hoch", "mittel", "gering", "sehr gering"]
-
-# ==============================================================================
-#                       Path for inputs and outputs
-# ==============================================================================
-
-# Automatic Data Imports
-base_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-input_profile_path = os.path.join(base_path, "tek_data", "GHD_profile.xlsx")
-input_dhw_path = os.path.join(base_path, "tek_data", "DHW_profile.xlsx")
-input_energy_path = os.path.join(base_path, "tek_data",
-                                 "TEK_Teilenergiekennwerte.xlsx")
-input_zone_path = os.path.join(base_path, "tek_data", "GHD_Zonierung.xlsx")
-input_tabula_path = os.path.join(base_path, "tek_data", "TABULA_data.xlsx")
-output_path = os.path.join(base_path, "data", "tek_data", "output_heat_profile")
-
-
-def gen_heat_profile(building_typ,
-                     area,
-                     temperature_profile,
-                     year=2021,
-                     energy_typ="mittel",
-                     plot=False,
-                     save_plot=False):
-    """
-    total_degree_day: K*h
-    annual_value: kW*h, jährlicher Gesamt Heizwärmebedarf
-    Using degree day method to calculate the heat profil, the set temperature depending
-    on room type and heating start at the temperature of 15 degree.
-    :return:
-    """
-    # Analysis thermal zones in building
-    new_zone_df = analysis_bld_zone(building_typ, area)
-
-    # Calculate demand in each zone and degree day method
-    demand_df = pd.read_excel(input_energy_path, sheet_name=energy_typ)
-    profile_df = pd.read_excel(input_profile_path, sheet_name='DIN V 18599')
-    total_heat_profile = []
-    total_heat_demand = 0
-    for row in range(len(new_zone_df)):
-        zone = new_zone_df.loc[row, 'DIN_Zone']  # Zone name in DIN
-        hour_status = op_time_status(year, zone)
-        zone_area = new_zone_df.loc[row, 'new_area']
-        zone_heat_demand = calc_zone_demand(demand_df, 'heat', zone, zone_area)
-        zone_heat_profile = degree_day(zone, zone_heat_demand, profile_df,
-                                       temperature_profile, hour_status)
-        total_heat_profile = np.sum([total_heat_profile, zone_heat_profile],
-                                    axis=0)
-        total_heat_demand += zone_heat_demand
-
-    if plot:
-        plot_profile(total_heat_profile, save_plot)
-
-    return total_heat_profile
-
-
-def gen_hot_water_profile(building_typ, area, year=2021, energy_typ="mittel"):
-    # new_zone_df = analysis_bld_zone(building_typ, area)
-    # for row in range(len(new_zone_df)):
-    #     zone = new_zone_df.loc[row, 'DIN_Zone']
-
-    bld_hot_water_demand = calc_bld_demand(building_typ, area, 'hot_water',
-                                           energy_typ)
-    if building_typ == "Wohngebäude" or building_typ == "Wohngebäude (MFH)":
-        hot_water_heating_demand_df = pd.read_excel(input_dhw_path,
-                                                    sheet_name='DHW',
-                                                    header=None,
-                                                    usecols=[1],
-                                                    skiprows=1)
-        hot_water_heating_demand_df.columns = ['Wärmebedarf für '
-                                               'Trinkwassererwärmung (kWh)']
-        # The data is for 300 liter hot water demand and 60 grade Celsius supply
-        # hot water with 12 grade Celsius inlet water.
-        hot_water_heating_demand_df['Aktueller Wärmebedarf für ' \
-                                    'Trinkwassererwärmung (kWh)'] = \
-            hot_water_heating_demand_df['Wärmebedarf für ' \
-                                        'Trinkwassererwärmung (kWh)'].map(
-                lambda x: x / (4180 * 300 * (60 - 12) / 3600 / 1000 * 365) *
-                          bld_hot_water_demand)
-
-        hot_water_heating_demand_array = np.array(
-            hot_water_heating_demand_df['Aktueller Wärmebedarf für '
-                                        'Trinkwassererwärmung (kWh)'])
-
-    elif building_typ in building_typ_list and building_typ != "Wohngebäude" \
-            or building_typ != "Wohngebäude (MFH)":
-        bld_occupancy = calc_bld_occupancy(building_typ, area, year)
-        hot_water_heating_demand_array = bld_hot_water_demand / sum(
-            bld_occupancy) * np.array(bld_occupancy)
-        # print(hot_water_heating_demand_array)
-
-    return hot_water_heating_demand_array
-
-
-def op_time_status(year, zone):
-    """
-    Calculate the operating time for whole year. The weekend and work time
-    could be considered in this function. For different thermal zone the
-    operating time also varies according to DIN V 18599.
-    Args:
-        year: int, target year
-        zone: str, the name should be same as in the standard DIN V 18599
-    Returns:
-        List of status for each hour in whole year
-    """
-    weekday_list = find_weekday(year)
-    profile_df = pd.read_excel(input_profile_path, sheet_name='DIN V 18599')
-    start_time = profile_df.loc[profile_df['Raumtyp'] == zone][
-        'Nutzungszeit_von'].values[0]
-    end_time = profile_df.loc[profile_df['Raumtyp'] == zone][
-        'Nutzungzeit_bis'].values[0]
-    if end_time == 0:
-        end_time = 24  # 24:00 is 0:00
-
-    status_list = []
-    if profile_df.loc[profile_df['Raumtyp'] == zone]['Nutzungstage'].values[
-        0] in [150, 200, 230, 250]:
-        # The zone are only used in weekday. Zone such as audience hall
-        # with 150 operating days and Zone auch as fabric with 230 operating
-        # days are also considered as the other working zone.
-        day = 0
-        while day < 365:
-            day_status = [0] * 24
-            if weekday_list[day] in [0, 1, 2, 3, 4]:
-                day_status[start_time:end_time] = [1] * (end_time - start_time)
-            status_list += day_status
-            day += 1
-    elif profile_df.loc[profile_df['Raumtyp'] == zone]['Nutzungstage'].values[
-        0] == 300:
-        # The zone which are only used in weekday and Saturday, like restaurant
-        day = 0
-        while day < 365:
-            day_status = [0] * 24
-            if weekday_list[day] in [0, 1, 2, 3, 4, 5]:
-                day_status[start_time:end_time] = [1] * (end_time - start_time)
-            status_list += day_status
-            day += 1
-    elif profile_df.loc[profile_df['Raumtyp'] == zone]['Nutzungstage'].values[
-        0] == 365:
-        # The zone are used everyday, such as bedroom
-        day = 0
-        while day < 365:
-            day_status = [0] * 24
-            day_status[start_time:end_time] = [1] * (end_time - start_time)
-            status_list += day_status
-            day += 1
-    else:
-        # The zone such as hall are not considered
-        warn('The operating days of zone' + zone + 'does not match the DIN V '
-                                                   '18599')
-
-    return status_list
-
-
-def find_weekday(year):
-    """
-    Create a list of weekdays throughout the year. The holidays are not
-    considered in the function.
-    Leap years are also considered to have only 365 days to reduce the work.
-    Args:
-        year: int, target year
-    Returns:
-        list, status for whole year
-    """
-    # weekday() == 4 means Friday, the value could be from 0 to 6
-    day = datetime.date(year, 1, 1).weekday()
-    weekday_list = []
-
-    i = 0
-    while i < 365:
-        weekday_list.append(day)
-        day = (day + 1) % 7
-        i += 1
-
-    return weekday_list
-
-
-def analysis_bld_zone(building_typ, area):
-    """Analysis the thermal zones in building, the zone, which is smaller
-    than the min_zone_area should be ignored. The min_zone_area is hard coded
-    with 2 m², which could be fixed later or not :)"""
-    zone_df = pd.read_excel(input_zone_path, sheet_name=building_typ,
-                            header=None, usecols=range(5), skiprows=2)
-    zone_df.columns = ['Nr', 'Zone', 'Percentage', 'kum_per', 'DIN_Zone']
-    # 1st try to calculate the area of each zone
-    zone_df['Area'] = zone_df['Percentage'].map(lambda x: x * area)
-
-    # Too small zones should be delete
-    min_zone_area = 2
-    new_zone_df = zone_df[zone_df['Area'] > min_zone_area]
-
-    # Recalculate percentage
-    sum_per = new_zone_df['Percentage'].sum()
-    pd.options.mode.chained_assignment = None
-    new_zone_df['new_per'] = new_zone_df['Percentage'].map(
-        lambda x: x / sum_per)
-    new_zone_df['new_area'] = new_zone_df['Area'].map(lambda x: x / sum_per)
-
-    return new_zone_df
-
-
-def calc_bld_demand(building_typ, area, energy_commodity, energy_typ='mittel'):
-    """Calculate the total demand of the building by adding up the demand of
-    all thermal zones.
-    energy_commodity: the energy carrier, could be 'heat', 'cool', 'hot_water',
-    'elec', the name is defined in method calc_zone_demand.
-    energy_typ: the building energy typ, which is define by project TEK and
-    describe the building energy level. It could be the items in energy_typ_list
-    """
-    # Analysis thermal zones in building
-    new_zone_df = analysis_bld_zone(building_typ, area)
-
-    bld_demand = 0
-    demand_df = pd.read_excel(input_energy_path, sheet_name=energy_typ)
-    for row in range(len(new_zone_df)):
-        zone = new_zone_df.loc[row, 'DIN_Zone']
-        zone_area = new_zone_df.loc[row, 'new_area']
-        zone_demand = calc_zone_demand(demand_df, energy_commodity, zone,
-                                       zone_area)
-        bld_demand += zone_demand
-
-    return bld_demand
-
-
-def calc_zone_demand(demand_df, demand_typ, zone_typ, zone_area):
-    """Calculate the annual total heat demand for each thermal zone"""
-    total_demand = 0  # Unit kWh
-
-    column_name = ''
-    column_name_light = 'Beleuchtung'
-    column_name_other = 'Arbeitshilfen'
-    if demand_typ == 'heat':
-        column_name = 'Heizung'
-    elif demand_typ == 'cool':
-        column_name = 'Kühlkälte'
-    elif demand_typ == 'hot_water':
-        column_name = 'Warmwasser'
-    elif demand_typ == 'elec':
-        pass
-    else:
-        warn('The demand typ is not allowed!')
-
-    if demand_typ == 'elec':
-        zone_demand = demand_df[demand_df['Standard-Nutzungseinheiten'] ==
-                                zone_typ][column_name_light].values[0] + \
-                      demand_df[demand_df['Standard-Nutzungseinheiten'] ==
-                                zone_typ][column_name_other].values[0]
-    else:
-        zone_demand = demand_df[demand_df['Standard-Nutzungseinheiten'] ==
-                                zone_typ][column_name].values[0]
-    total_demand += zone_demand * zone_area
-
-    return total_demand
-
-
-def calc_bld_occupancy(building_typ,
-                       area,
-                       year):
-    """According to standard SIA2024, the occupancy for each hour are given
-    for each thermal zone"""
-    din_table = pd.read_excel(input_profile_path, sheet_name='DIN V 18599')
-    sia_table = pd.read_excel(input_profile_path, sheet_name='SIA2024')
-    bld_occupancy = [0] * 8760
-
-    new_zone_df = analysis_bld_zone(building_typ, area)
-    # print(new_zone_df)
-
-    for row in range(len(new_zone_df)):
-        din_zone = new_zone_df.loc[row, 'DIN_Zone']
-        sia_zone = din_table.loc[
-            din_table['Raumtyp'] == din_zone, 'Raumtyp_SIA'].values[0]
-        hour_status = op_time_status(year, din_zone)
-        # print(sia_zone)
-        zone_area = new_zone_df.loc[row, 'new_area']
-        sia_pers = sia_table.loc[
-            sia_table['Raumtyp'] == sia_zone, 'Personenfläche'].values[0]
-        # print(sia_pers)
-        sia_pers_profile = sia_table.loc[
-            sia_table['Raumtyp'] == sia_zone, 'Personenbelegung'].values[0]
-        sia_pers_profile = sia_pers_profile.split(', ')
-        sia_pers_profile = list(map(float, sia_pers_profile))
-        # print(sia_pers_profile)
-        if not np.isnan(sia_pers):
-            zone_pers = zone_area / sia_pers
-            # print(zone_pers)
-            zone_pers_profile = zone_pers * np.array(sia_pers_profile)
-            zone_occupancy = [0] * 8760
-
-            for day in range(365):
-                for hour in range(24):
-                    if hour_status[day*24+hour] == 1:
-                        zone_occupancy[day*24+hour] = zone_pers_profile[hour]
-            # print(zone_pers_profile)
-            bld_occupancy += np.array(zone_occupancy)
-    # print(bld_occupancy)
-    return bld_occupancy
-
-
-def degree_day(zone_typ, annual_value, profile_df, temperature_profile,
-               status_list):
-    heat_profile = []
-    start_temp = 15  # The limit for heating on or off, could be the same as
-    # set temperature? 15 comes from the german
-    set_temp_heat = profile_df[profile_df['Raumtyp'] == zone_typ][
-        'Raum-Solltemperatur_Heizung '].values[0]
-
-    total_degree_day = 0
-    for time_step in range(8760):
-        if temperature_profile[time_step] < start_temp and \
-                status_list[time_step] == 1 and \
-                temperature_profile[time_step] < set_temp_heat:
-            total_degree_day += (set_temp_heat - temperature_profile[
-                time_step])
-
-    for time_step in range(8760):
-        if temperature_profile[time_step] < start_temp and \
-                status_list[time_step] == 1 and \
-                temperature_profile[time_step] < set_temp_heat:
-            heat_profile.append(
-                (set_temp_heat - temperature_profile[time_step]) /
-                total_degree_day * annual_value)
-        else:
-            heat_profile.append(0)
-
-    return heat_profile
-
-
-def plot_profile(heat_profile, save_plot=False):
-    plt.figure()
-    plt.plot(heat_profile)
-    plt.ylabel('Heat Profile')
-    plt.xlabel('Hours [h]')
-    plt.ylim(ymin=0)
-    plt.xlim(xmin=0)
-    plt.grid()
-    if save_plot:
-        plt.savefig(os.path.join(output_path, 'heat_profile_figure.jpg'))
-    plt.show()
-
-
-def calc_residential_demand(bld_type, bld_year, bld_area,
-                            method='TABULA Berechnungsverfahren / korrigiert '
-                                   'auf Niveau von Verbrauchswerten',
-                            scenario='Ist-Zustand'):
-    """According to the IWU TABULA calculate the space heating demand and hot
-    water demand. The details of the method could be found in the project
-    report 'DE_TABULA_TypologyBrochure_IWU.pdf'
-    bld_type: building type, could be 'SFH', 'MFH', 'TH', 'AB'
-    bld_year: building construction year, used to find the building class
-    bld_area: area of the building
-    """
-    tabula_df = pd.read_excel(input_tabula_path)
-    bld = tabula_df[(tabula_df['Gebäudetyp'] == bld_type) &
-                    (tabula_df['Baualtersklasse_von'] < bld_year) &
-                    (tabula_df['Baualtersklasse_bis'] >= bld_year) &
-                    (tabula_df['Berechnungsverfahren'] == method) &
-                    (tabula_df['Szenario'] == scenario)]
-
-    heating_demand = bld['Heizung (Wärmeerzeugung)'].values[0] * bld_area
-    hot_water_demand = bld['Warmwasser (Wärmeerzeugung)'].values[0] * bld_area
-
-    return heating_demand, hot_water_demand
-
-
-if __name__ == "__main__":
-    input_temp_path = os.path.join(base_path, 'tek_data', 'temperature.csv')
-    temperature = pd.read_csv(input_temp_path)['temperature'].values
-    # gen_heat_profile("Wohngebäude", 300, temperature, plot=True)
-    # print('Space heating demand profile:')
-    # print(gen_heat_profile("Wohngebäude (MFH)", 300, temperature))
-    print('Hot water demand profile:')
-    print(gen_hot_water_profile("Verwaltungsgebäude", 300))
-
-    # calc_bld_occupancy("Verwaltungsgebäude", 300, year=2021)
diff --git a/demand_generator/tek_data/temperature.csv b/demand_generator/tek_data/temperature.csv
deleted file mode 100644
index df17ec0bd6c7a4ceb496df8c4c3fd91da8d88fe3..0000000000000000000000000000000000000000
--- a/demand_generator/tek_data/temperature.csv
+++ /dev/null
@@ -1,8761 +0,0 @@
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-1289,2.5
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-1296,2.2
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-1306,6.7
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-1309,7.9
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-1311,6.8
-1312,6.4
-1313,6.1
-1314,5.7
-1315,5.2
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-1317,4.6
-1318,4.6
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-1359,8.4
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-1361,7.3
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-1376,1.2
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-1384,8.9
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-1390,9.2
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-1503,1.3
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-1506,1.5
-1507,1.4
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-1528,4.9
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-1567,-0.8
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-1577,5.6
-1578,5.6
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-1586,5.4
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-1591,3.4
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-1594,5.8
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-1597,10
-1598,10.3
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-1602,10.3
-1603,9.9
-1604,9.3
-1605,8.3
-1606,8.5
-1607,8.8
-1608,9
-1609,9.2
-1610,9
-1611,8
-1612,7.4
-1613,8.4
-1614,8.1
-1615,7.6
-1616,8.1
-1617,8.2
-1618,8.4
-1619,8.9
-1620,9.3
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-1622,9.7
-1623,11.1
-1624,10.5
-1625,9.6
-1626,8.6
-1627,8.8
-1628,8.7
-1629,8.6
-1630,8.6
-1631,9
-1632,9.5
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-1636,10.7
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-1645,7.4
-1646,7.3
-1647,7.1
-1648,6.9
-1649,6.6
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-1651,6.3
-1652,6.1
-1653,6
-1654,5.7
-1655,5.2
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-1657,5.1
-1658,5.2
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-1663,4.9
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-1666,5
-1667,5.4
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-1672,5.4
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-1675,6
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-1685,7.9
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-1688,7.8
-1689,7.6
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-1697,6.6
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-1708,4.1
-1709,3.9
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-1716,5.5
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-1724,7.2
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-1727,7
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-1733,6.6
-1734,6.3
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-1738,6.5
-1739,6.6
-1740,6.8
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-1742,7.1
-1743,7.4
-1744,7.6
-1745,7.6
-1746,7.3
-1747,7.7
-1748,8
-1749,8
-1750,7.8
-1751,7.8
-1752,7.9
-1753,7.9
-1754,8.1
-1755,8.2
-1756,8.4
-1757,8.5
-1758,8.6
-1759,8.5
-1760,8.3
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-1764,6.8
-1765,6.4
-1766,6.5
-1767,6.7
-1768,6.7
-1769,6.6
-1770,6.8
-1771,5.6
-1772,6
-1773,4.8
-1774,5.2
-1775,5
-1776,4.9
-1777,4.5
-1778,3.5
-1779,3.6
-1780,4.9
-1781,3.7
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-1783,4.4
-1784,4.5
-1785,4.3
-1786,4.7
-1787,5
-1788,5.1
-1789,5.5
-1790,5.1
-1791,4.7
-1792,6
-1793,5.8
-1794,5.2
-1795,4.1
-1796,6.1
-1797,6.9
-1798,7.2
-1799,7.1
-1800,6.6
-1801,6.5
-1802,6.3
-1803,6.3
-1804,5.7
-1805,5.5
-1806,5.4
-1807,4.7
-1808,4.9
-1809,5.1
-1810,5
-1811,5.6
-1812,5.5
-1813,5.5
-1814,5.6
-1815,5.6
-1816,5.8
-1817,5.8
-1818,6
-1819,6
-1820,5.9
-1821,6.3
-1822,6.8
-1823,6.9
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-1825,7.4
-1826,7.4
-1827,7.2
-1828,7.3
-1829,7.2
-1830,7.3
-1831,7.3
-1832,7
-1833,6.9
-1834,6.7
-1835,6.7
-1836,6.9
-1837,7.2
-1838,6.9
-1839,6.7
-1840,6.6
-1841,6.4
-1842,6.3
-1843,6.1
-1844,6.2
-1845,6
-1846,6.3
-1847,6.2
-1848,6.4
-1849,6.3
-1850,6.3
-1851,6.3
-1852,6.2
-1853,5.8
-1854,5.7
-1855,6.1
-1856,6.3
-1857,6.2
-1858,6.3
-1859,5.5
-1860,5.8
-1861,5.7
-1862,5.4
-1863,5.3
-1864,5.8
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-1866,6.2
-1867,6.2
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-1869,6
-1870,5.7
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-1872,4.6
-1873,3.5
-1874,3.2
-1875,3.1
-1876,2.7
-1877,2.5
-1878,2.7
-1879,2.3
-1880,2.4
-1881,3.3
-1882,4.6
-1883,5.4
-1884,5.4
-1885,5.6
-1886,5.6
-1887,5.8
-1888,5.9
-1889,6
-1890,5.5
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-1892,5.3
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-1894,4.2
-1895,4.1
-1896,4.5
-1897,4.1
-1898,3.8
-1899,3.5
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-1902,4.5
-1903,4.7
-1904,4.9
-1905,5.3
-1906,5.4
-1907,6
-1908,6.3
-1909,6.1
-1910,6
-1911,6.7
-1912,6.9
-1913,7.2
-1914,7.1
-1915,7.1
-1916,6.6
-1917,5.6
-1918,5.6
-1919,5.4
-1920,5.4
-1921,5.1
-1922,5.2
-1923,5.5
-1924,5.6
-1925,5.6
-1926,5.4
-1927,5.2
-1928,5
-1929,5.4
-1930,6
-1931,6.1
-1932,6.6
-1933,7.5
-1934,8.1
-1935,7.9
-1936,7.5
-1937,7.6
-1938,7.8
-1939,7.5
-1940,7.3
-1941,7.2
-1942,7.1
-1943,7.1
-1944,7.3
-1945,7.8
-1946,7.5
-1947,7.5
-1948,7
-1949,6.5
-1950,6.3
-1951,5.8
-1952,6.1
-1953,6.8
-1954,7.9
-1955,8.7
-1956,8.9
-1957,9
-1958,8.9
-1959,9.1
-1960,8.7
-1961,9.3
-1962,9.5
-1963,8.4
-1964,8.9
-1965,7.7
-1966,7
-1967,7
-1968,6.9
-1969,6.6
-1970,6.2
-1971,6.1
-1972,5.6
-1973,5.7
-1974,5.7
-1975,4
-1976,4
-1977,4.3
-1978,5.1
-1979,5.7
-1980,6.7
-1981,7.4
-1982,8.1
-1983,8.7
-1984,9
-1985,9.3
-1986,8.9
-1987,8.4
-1988,7.3
-1989,6.1
-1990,5.6
-1991,5.5
-1992,5.3
-1993,4.8
-1994,5
-1995,4.8
-1996,4.3
-1997,3.8
-1998,3.4
-1999,3.6
-2000,4.4
-2001,5.1
-2002,6.3
-2003,7.3
-2004,8
-2005,8.5
-2006,8.6
-2007,9
-2008,8.3
-2009,7.3
-2010,6.5
-2011,6.4
-2012,6.3
-2013,6.3
-2014,6.1
-2015,6
-2016,6.1
-2017,6.3
-2018,6.4
-2019,6.8
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-2021,7.7
-2022,7.3
-2023,6.8
-2024,6.6
-2025,7.8
-2026,8.5
-2027,9.3
-2028,10.2
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-2031,11.9
-2032,11.4
-2033,11.4
-2034,11
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-2036,9
-2037,8.3
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-2039,7.8
-2040,7.8
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-2042,8.2
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-2047,8.7
-2048,8.9
-2049,9.5
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-2088,7.8
-2089,7.8
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-2097,5.2
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-2200,17
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-2215,6.2
-2216,8.1
-2217,10.8
-2218,12.8
-2219,15.3
-2220,17.2
-2221,18.2
-2222,19.2
-2223,18.5
-2224,18.6
-2225,19.7
-2226,19.3
-2227,17.9
-2228,16.2
-2229,15.3
-2230,14.7
-2231,13.7
-2232,13.3
-2233,12.1
-2234,11.4
-2235,10.8
-2236,10.3
-2237,10
-2238,9.7
-2239,9.9
-2240,10.7
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-2243,16.3
-2244,16.9
-2245,18.5
-2246,20.2
-2247,19.2
-2248,18.5
-2249,16.7
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-2251,16.2
-2252,14.5
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-2254,10.5
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-2258,8.2
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-2260,7.4
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-2262,7.1
-2263,7.1
-2264,7.9
-2265,8.1
-2266,8.6
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-2268,10.5
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-2272,12.1
-2273,11.5
-2274,11.5
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-2278,7.7
-2279,6.7
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-2285,3.4
-2286,3.7
-2287,4.3
-2288,5.4
-2289,5.9
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-2291,4.9
-2292,5.3
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-2294,5.3
-2295,5.3
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-2297,5
-2298,4.6
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-2300,3.5
-2301,3.6
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-2305,2.7
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-2314,4.5
-2315,4.6
-2316,5.3
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-2320,6.3
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-2329,3.9
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-2339,7
-2340,8
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-2342,9.6
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-2349,8.5
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-2353,6.5
-2354,5.9
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-2361,7.4
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-2363,11
-2364,12.7
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-2369,10.8
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-2374,7.4
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-2381,6.6
-2382,6.6
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-2384,6
-2385,7
-2386,8
-2387,8.5
-2388,9.3
-2389,9.9
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-2392,9.9
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-2394,10.7
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-2397,7.6
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-2404,7.2
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-2407,7.9
-2408,7.6
-2409,7.6
-2410,6.6
-2411,6.6
-2412,6.2
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-2415,6.2
-2416,6.5
-2417,6.7
-2418,6.7
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-2422,6.3
-2423,6.2
-2424,6.2
-2425,6.2
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-2427,6.1
-2428,5.9
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-2432,6.2
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-2437,7.3
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-2439,7
-2440,6.9
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-2444,6
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-2446,5.9
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-2450,3.9
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-2456,5.3
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-2464,6.8
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-2472,7
-2473,7.3
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-2476,7.5
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-2480,7.7
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-2485,7.7
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-2505,5.8
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-2507,6.7
-2508,6.6
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-2510,7.3
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-2512,7.1
-2513,6.6
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-2518,5.6
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-2523,4.3
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-2525,3.8
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-2530,7.6
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-2534,8.4
-2535,8.6
-2536,8.6
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-2539,7.8
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-2547,4.8
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-2553,6.7
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-2559,9.3
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-2561,8.7
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-2563,7.6
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-2566,5.8
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-2574,7.3
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-2626,8.9
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-2631,13
-2632,12.9
-2633,13.1
-2634,12.9
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-2638,9.3
-2639,8.7
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-2644,6.9
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-2653,7.8
-2654,8.8
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-2667,9.7
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-2674,11.7
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-2679,13.7
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-2693,8.1
-2694,7.9
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-2707,14.9
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-2715,8.8
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-2718,8
-2719,8.3
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-2726,19
-2727,19
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-2729,16.9
-2730,14.7
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-2734,12.7
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-2740,10.7
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-2757,12.8
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-2779,15.1
-2780,13.9
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-2816,8.7
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-2923,17.9
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-2940,18.7
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-2943,23.2
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-2948,11.7
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-3086,9
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-3119,11.5
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-3142,13.5
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-4077,15
-4078,14.5
-4079,14
-4080,13.6
-4081,13.8
-4082,13.5
-4083,12.9
-4084,12.9
-4085,12.8
-4086,13
-4087,13.4
-4088,14.9
-4089,15.9
-4090,14.9
-4091,15.6
-4092,15.5
-4093,15.4
-4094,15.3
-4095,15.5
-4096,15.5
-4097,14.9
-4098,14.9
-4099,14.9
-4100,14.1
-4101,13.6
-4102,13.1
-4103,13.2
-4104,13.7
-4105,12.8
-4106,12.7
-4107,12.6
-4108,12.6
-4109,12.4
-4110,12.2
-4111,13.2
-4112,13.6
-4113,15.1
-4114,15.1
-4115,15.4
-4116,14.8
-4117,15
-4118,14.8
-4119,15.2
-4120,15.5
-4121,15.2
-4122,15.5
-4123,15
-4124,14.5
-4125,14.3
-4126,14.2
-4127,14
-4128,13.2
-4129,13.1
-4130,12.9
-4131,12.8
-4132,12.1
-4133,11.9
-4134,12.2
-4135,12.8
-4136,13.6
-4137,13.2
-4138,14.2
-4139,14.9
-4140,15.1
-4141,15.1
-4142,14.8
-4143,14.4
-4144,14.6
-4145,14.2
-4146,14.1
-4147,13.8
-4148,13.8
-4149,13.4
-4150,13.3
-4151,12.9
-4152,12.6
-4153,12.5
-4154,11.9
-4155,11.7
-4156,11.4
-4157,11.5
-4158,12.6
-4159,13.1
-4160,14.1
-4161,14.7
-4162,15.4
-4163,15.8
-4164,15.4
-4165,14.3
-4166,13.5
-4167,13.8
-4168,13.1
-4169,12.6
-4170,12.6
-4171,12.8
-4172,12.5
-4173,12.3
-4174,11.9
-4175,13.2
-4176,14.8
-4177,17.3
-4178,19.2
-4179,20.7
-4180,20.6
-4181,20.7
-4182,20.5
-4183,21
-4184,22.1
-4185,22.6
-4186,23.4
-4187,23.5
-4188,23.9
-4189,28.1
-4190,24
-4191,23.8
-4192,25.2
-4193,24.3
-4194,26
-4195,25.9
-4196,27.8
-4197,27.2
-4198,24.8
-4199,23.5
-4200,23.3
-4201,23
-4202,21.9
-4203,21.2
-4204,21
-4205,20.7
-4206,21
-4207,20.6
-4208,17.6
-4209,17
-4210,16.4
-4211,16.4
-4212,16.5
-4213,16.8
-4214,18.5
-4215,18.3
-4216,17.7
-4217,17
-4218,17
-4219,16.1
-4220,16.5
-4221,16.2
-4222,15.7
-4223,15.4
-4224,15.3
-4225,15.8
-4226,16
-4227,16
-4228,15.7
-4229,15.5
-4230,15.9
-4231,15.9
-4232,15.8
-4233,16.3
-4234,16.3
-4235,16.6
-4236,16.5
-4237,16.2
-4238,15.9
-4239,15.6
-4240,16.3
-4241,16.1
-4242,15.9
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-4244,15.5
-4245,14.6
-4246,14.1
-4247,13.6
-4248,13.3
-4249,13.3
-4250,13.2
-4251,13.2
-4252,13.5
-4253,13.6
-4254,13.2
-4255,13.6
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-4257,14.8
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-4261,15.9
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-4263,15.4
-4264,15.5
-4265,15.3
-4266,15.6
-4267,16.1
-4268,15.9
-4269,15.8
-4270,15.9
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-4272,13.8
-4273,14
-4274,14.1
-4275,14.5
-4276,16.3
-4277,16.1
-4278,16.3
-4279,16.1
-4280,16.1
-4281,15.6
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-4283,15.5
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-4286,15.5
-4287,15.8
-4288,15.4
-4289,15.4
-4290,16.1
-4291,16
-4292,15.3
-4293,14.6
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-4295,13.7
-4296,13.7
-4297,13.8
-4298,13.9
-4299,13.2
-4300,12.7
-4301,13.5
-4302,13.6
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-4304,15.7
-4305,16.2
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-4307,17.2
-4308,18.2
-4309,19.7
-4310,20.3
-4311,21.6
-4312,21.4
-4313,21.9
-4314,22.5
-4315,22.8
-4316,20.3
-4317,19.6
-4318,19
-4319,18.2
-4320,17.4
-4321,16.6
-4322,15.8
-4323,15.2
-4324,14.9
-4325,15.1
-4326,15.9
-4327,16.6
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-4330,19.6
-4331,19.7
-4332,20
-4333,20
-4334,19.7
-4335,19.7
-4336,19
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-4343,17
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-4349,15.4
-4350,14.9
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-4355,17.4
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-4358,20.2
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-4360,19.8
-4361,19.5
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-4363,19.1
-4364,18.7
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-4367,17.1
-4368,16.7
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-4373,15.7
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-4377,18.6
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-4380,19.8
-4381,20.4
-4382,21.7
-4383,23.2
-4384,23.7
-4385,23.3
-4386,22.8
-4387,22.5
-4388,21.9
-4389,22.1
-4390,21.7
-4391,21.4
-4392,20.9
-4393,20.3
-4394,20.1
-4395,19.5
-4396,19.2
-4397,19.1
-4398,19.3
-4399,19.5
-4400,20.1
-4401,20.5
-4402,21.2
-4403,21.5
-4404,21.4
-4405,22.4
-4406,22.5
-4407,22.6
-4408,22.9
-4409,23.2
-4410,23
-4411,22.6
-4412,22
-4413,21.4
-4414,20.9
-4415,19.3
-4416,17.6
-4417,15.5
-4418,14
-4419,12.9
-4420,13.1
-4421,13.4
-4422,13.9
-4423,14.4
-4424,14.1
-4425,15.4
-4426,15.7
-4427,17.8
-4428,18.1
-4429,14.5
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-4434,14.4
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-4439,14.6
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-4444,14.6
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-4446,14.4
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-4453,17.5
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-4458,17.6
-4459,17.6
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-4462,15.7
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-4465,15.2
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-4472,13.8
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-4476,15.2
-4477,17.2
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-4480,15.3
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-4482,15.2
-4483,15.6
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-4486,15.3
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-4488,15.2
-4489,15.2
-4490,15.3
-4491,15.2
-4492,15.2
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-4494,14.9
-4495,14.8
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-4497,15.1
-4498,15.5
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-4500,16.5
-4501,17.7
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-4503,16.5
-4504,18.2
-4505,16.9
-4506,16.9
-4507,16.6
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-4509,15.3
-4510,15.1
-4511,14.6
-4512,14.3
-4513,13.9
-4514,13.8
-4515,13.4
-4516,13.5
-4517,13.7
-4518,14
-4519,15.1
-4520,16.3
-4521,17.2
-4522,18.5
-4523,19
-4524,19.9
-4525,19.8
-4526,18.6
-4527,17.6
-4528,17.2
-4529,16.8
-4530,16.7
-4531,16.6
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-4533,16
-4534,15.4
-4535,14.9
-4536,14.6
-4537,13.9
-4538,14.3
-4539,14.3
-4540,13.7
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-4542,14.5
-4543,14.9
-4544,15.7
-4545,15.7
-4546,15
-4547,15.5
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-4551,16.8
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-4553,16.7
-4554,17.6
-4555,16.9
-4556,16.2
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-4562,12.8
-4563,12.8
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-4565,12.6
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-4568,14.5
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-4571,19.2
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-4575,17.5
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-4577,16.3
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-4583,14.7
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-4586,12.6
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-4593,13.8
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-4609,14.2
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-4616,14.6
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-4624,17.4
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-4634,15.1
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-4654,18.6
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-4662,18.9
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-4664,22.2
-4665,23.6
-4666,25.1
-4667,26.8
-4668,28.1
-4669,26
-4670,25.4
-4671,25.9
-4672,24.9
-4673,23.8
-4674,23.7
-4675,22.9
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-4680,18
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-4689,20.6
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-4698,26
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-4701,23.8
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-4709,18
-4710,19
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-4712,22.2
-4713,24.7
-4714,28.2
-4715,29.1
-4716,29
-4717,27.7
-4718,28.4
-4719,22.1
-4720,23.9
-4721,22
-4722,21.9
-4723,24
-4724,23.2
-4725,23.1
-4726,22.4
-4727,20.8
-4728,18.9
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-4730,14.8
-4731,13.4
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-4733,13.1
-4734,12.7
-4735,12.7
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-4737,15
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-4750,16.4
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-4809,19.9
-4810,22
-4811,23.5
-4812,25
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-4814,27.8
-4815,28.8
-4816,29.3
-4817,30.3
-4818,30.3
-4819,30
-4820,29.5
-4821,28.1
-4822,25.9
-4823,24.9
-4824,24
-4825,23.7
-4826,23.6
-4827,23.8
-4828,23.9
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-4830,22.5
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-4833,26.9
-4834,26.3
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-4837,25.6
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-4839,25.4
-4840,24.7
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-4842,22.5
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-4847,18.5
-4848,17.8
-4849,16.9
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-4863,21.4
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-4869,19.8
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-4884,24.1
-4885,21.9
-4886,22.8
-4887,20.5
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-4901,15.7
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-4908,17.5
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-4914,16.7
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-4922,14.3
-4923,14.6
-4924,13.9
-4925,13.7
-4926,14.1
-4927,14.5
-4928,15.3
-4929,16.2
-4930,17.1
-4931,17.3
-4932,18
-4933,17
-4934,16.8
-4935,17
-4936,17.1
-4937,17.3
-4938,17.3
-4939,17
-4940,16.5
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-4945,14
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-4947,12.5
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-4949,11.7
-4950,12
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-4953,15
-4954,16
-4955,17.5
-4956,18.3
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-4959,19.3
-4960,19.8
-4961,20.7
-4962,21.2
-4963,21
-4964,20.1
-4965,19.3
-4966,18.4
-4967,17.4
-4968,16.6
-4969,15.6
-4970,14.6
-4971,13.7
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-4973,13.4
-4974,12.9
-4975,13.9
-4976,14.7
-4977,15.9
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-4979,18.8
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-4981,19.9
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-4984,20
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-4987,20
-4988,19.7
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-4995,17.5
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-4998,17.2
-4999,17.7
-5000,18.6
-5001,19.4
-5002,21
-5003,21.3
-5004,23
-5005,26.1
-5006,23.1
-5007,22.5
-5008,22.5
-5009,21.6
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-5012,22.5
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-5015,20.2
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-5019,20.2
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-5024,21.5
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-5027,22.9
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-5030,26.1
-5031,25.6
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-5035,21.3
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-5037,20.1
-5038,19.5
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-5047,18.1
-5048,18.1
-5049,18.5
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-5052,18
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-5056,20.7
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-5059,19.8
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-5076,18.1
-5077,17.4
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-5116,14.6
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-5145,16.5
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-5192,14.4
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-5215,18.8
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-5217,22.5
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-5219,26.5
-5220,24.1
-5221,24.3
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-5224,24.9
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-5243,18.3
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-5257,16.1
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-5260,15.6
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-5272,20.6
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-5294,21.7
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-5296,22.1
-5297,22.7
-5298,21.6
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-5307,13.9
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-5314,18.3
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-5319,21.1
-5320,20.9
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-5323,20.3
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-5334,14
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-5339,23.9
-5340,24.8
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-5345,28.3
-5346,28
-5347,27.4
-5348,24.7
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-5350,21.5
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-5363,27.9
-5364,29.6
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-5366,31.3
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-5368,31.1
-5369,31.2
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-5374,25.4
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-5378,20.2
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-5414,19.7
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-5416,19.4
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-5427,16.1
-5428,15.9
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-5436,19.6
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-5447,18.1
-5448,18.6
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-5461,20.4
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-5463,20.3
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-5470,19
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-5481,17.9
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-5490,23.1
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-5508,26.4
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-5511,26.2
-5512,26.3
-5513,26.6
-5514,25.8
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-5516,24.2
-5517,22.8
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-5519,21.5
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-5522,19.4
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-5530,24.6
-5531,23.9
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-5535,26.4
-5536,25.7
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-5539,25.6
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-5598,15.2
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-5609,21.2
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-5629,19.3
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-5636,18.9
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-5654,18.9
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-5660,19
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-5689,17.8
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-5722,17.5
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-5729,16.7
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-5731,16.6
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-5824,16.9
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-5836,17
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-5840,18.2
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-5848,21.6
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-5867,17.6
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-5919,20.3
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-5926,18.9
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-8726,2
-8727,2.3
-8728,2.8
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-8757,0
-8758,-0.3
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-8760,-0.4
\ No newline at end of file
diff --git a/demand_generator/thermal_demand/SIA2024/GHD_Zonierung.xlsx b/demand_generator/thermal_demand/SIA2024/GHD_Zonierung.xlsx
deleted file mode 100644
index b9e092305a4a563736e8ac7bae91bf94767f6d6b..0000000000000000000000000000000000000000
Binary files a/demand_generator/thermal_demand/SIA2024/GHD_Zonierung.xlsx and /dev/null differ
diff --git a/demand_generator/thermal_demand/SIA2024/GHD_profile.xlsx b/demand_generator/thermal_demand/SIA2024/GHD_profile.xlsx
deleted file mode 100644
index 374862d905f41ec7949db65776a707a36b13b72a..0000000000000000000000000000000000000000
Binary files a/demand_generator/thermal_demand/SIA2024/GHD_profile.xlsx and /dev/null differ
diff --git "a/demand_generator/thermal_demand/SIA2024/IWU_Standardzonierungen f\303\274r die Hauptkategorien von NWG in Anlehnung an BWZK_v02.xlsx" "b/demand_generator/thermal_demand/SIA2024/IWU_Standardzonierungen f\303\274r die Hauptkategorien von NWG in Anlehnung an BWZK_v02.xlsx"
deleted file mode 100644
index ac7466897f52f1a9df7cad54bd8c84264f6f5c59..0000000000000000000000000000000000000000
Binary files "a/demand_generator/thermal_demand/SIA2024/IWU_Standardzonierungen f\303\274r die Hauptkategorien von NWG in Anlehnung an BWZK_v02.xlsx" and /dev/null differ
diff --git a/demand_generator/thermal_demand/SIA2024/TEK_Teilenergiekennwerte.xlsx b/demand_generator/thermal_demand/SIA2024/TEK_Teilenergiekennwerte.xlsx
deleted file mode 100644
index 00ea4697711a73b995ea2ed6624ef043474f4f57..0000000000000000000000000000000000000000
Binary files a/demand_generator/thermal_demand/SIA2024/TEK_Teilenergiekennwerte.xlsx and /dev/null differ
diff --git a/demand_generator/thermal_demand/SIA2024/gen_heat_profil.py b/demand_generator/thermal_demand/SIA2024/gen_heat_profil.py
deleted file mode 100644
index 0ffe2a49bb91ed7401b734ccf6f25484ff284f0d..0000000000000000000000000000000000000000
--- a/demand_generator/thermal_demand/SIA2024/gen_heat_profil.py
+++ /dev/null
@@ -1,188 +0,0 @@
-"""
-MIT License
-
-Copyright (c) 2023 RWTH Aachen University
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
-"""
-import os
-import csv
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import matplotlib as mpl
-
-# ==============================================================================
-#                     Non-residential Building List
-# ==============================================================================
-
-building_typ_list = ["Verwaltungsgebäude",
-                     "Büro und Dienstleistungsgebäude",
-                     "Hochschule und Forschung",
-                     "Gesundheitswesen",
-                     "Bildungseinrichtungen",
-                     "Kultureinrichtungen",
-                     "Sporteinrichtungen",
-                     "Beherbergen und Verpflegen",
-                     "Gewerbliche und industrielle",
-                     "Verkaufsstätten",
-                     "Technikgebäude"]
-energy_typ_list = ["sehr hoch", "hoch", "mittel", "gering", "sehr gering"]
-
-# ==============================================================================
-#                       Path for inputs and outputs
-# ==============================================================================
-
-# Automatic Data Imports
-base_path = os.path.dirname(os.path.abspath(__file__))
-input_profile_path = os.path.join(base_path, "GHD_profile.xlsx")
-input_energy_path = os.path.join(base_path, "TEK_Teilenergiekennwerte.xlsx")
-input_zone_path = os.path.join(base_path, "GHD_Zonierung.xlsx")
-output_path = os.path.join(base_path, 'output_heat_profile')
-
-# todo: The temperature profile should be taken from prosumer later
-input_temp_path = os.path.join(base_path, "temperature.csv")
-
-
-# ==============================================================================
-#                             Methods & Main
-# ==============================================================================
-def gen_heat_profile(building_typ,
-                     energy_typ,
-                     area,
-                     temperature_profile,
-                     plot=False,
-                     save_plot=False):
-    """
-    total_degree_day: K*h
-    annual_value: kW*h, jährlicher Gesamt Heizwärmebedarf
-    Using degree day method to calculate the heat profil, the set temperature depending
-    on room type and heating start at the temperature of 15 degree.
-    :return:
-    """
-    # ==========================================================================
-    #                   Analysis thermal zones in building
-    # ==========================================================================
-    zone_df = pd.read_excel(input_zone_path, sheet_name=building_typ,
-                            header=None, usecols=range(5), skiprows=2)
-    zone_df.columns = ['Nr', 'Zone', 'Percentage', 'kum_per', 'DIN_Zone']
-    # 1st try to calculate the area of each zone
-    zone_df['Area'] = zone_df['Percentage'].map(lambda x: x * area)
-
-    # Too small zones should be delete
-    min_zone_area = 2
-    new_zone_df = zone_df[zone_df['Area'] > min_zone_area]
-
-    # Recalculate percentage
-    sum_per = new_zone_df['Percentage'].sum()
-    pd.options.mode.chained_assignment = None
-    new_zone_df['new_per'] = new_zone_df['Percentage'].map(
-        lambda x: x / sum_per)
-    new_zone_df['new_area'] = new_zone_df['Area'].map(lambda x: x / sum_per)
-    # print(new_zone_df)
-
-    # ==========================================================================
-    #          Calculate demand in each zone and degree day method
-    # ==========================================================================
-    demand_df = pd.read_excel(input_energy_path, sheet_name=energy_typ)
-    profile_df = pd.read_excel(input_profile_path, sheet_name='DIN V 18599')
-    total_heat_profile = []
-    # print(profile_df)
-    for row in range(len(new_zone_df)):
-        zone = new_zone_df.loc[row, 'DIN_Zone']
-        zone_area = new_zone_df.loc[row, 'new_area']
-        zone_heat_demand = calc_heat_demand(demand_df, zone, zone_area,
-                                            energy_typ)
-        zone_heat_profile = degree_day(zone, zone_heat_demand, profile_df,
-                                       temperature_profile)
-        print(len(zone_heat_profile))
-        total_heat_profile = np.sum([total_heat_profile, zone_heat_profile],
-                                    axis=0)
-    print(total_heat_profile)
-    print(len(total_heat_profile))
-
-    if plot:
-        plot_profile(total_heat_profile, save_plot)
-
-    return total_heat_profile
-
-
-def calc_heat_demand(demand_df, zone_typ, zone_area, energy_typ='mittel'):
-    """Calculate the annual total heat demand"""
-    heat_demand = 0  # Unit kWh
-
-    zone_demand = demand_df[demand_df['Standard-Nutzungseinheiten'] ==
-                            zone_typ]['Heizung'].values[0]
-    heat_demand += zone_demand * zone_area
-    print("The heat demand of", zone_typ, "is", heat_demand, "kWh")
-
-    return heat_demand
-
-
-def degree_day(zone_typ, annual_value, profile_df, temperature_profile,
-               night_lower=False):
-    heat_profile = []
-    start_temp = 15  # The limit for heating on or off, could be the same as
-    # set temperature? 15 comes from the german
-    set_temp_heat = profile_df[profile_df['Raumtyp'] == zone_typ][
-        'Raum-Solltemperatur_Heizung '].values[0]
-
-    if night_lower:
-        night_lower_temp(zone_typ, profile_df)
-    else:
-        total_degree_day = 0
-        for temp in temperature_profile:
-            if temp < start_temp:
-                total_degree_day += (set_temp_heat - temp)
-
-        for temp in temperature_profile:
-            if temp < start_temp:
-                heat_profile.append(
-                    (set_temp_heat - temp) / total_degree_day * annual_value)
-            else:
-                heat_profile.append(0)
-
-    # print(heat_profile)
-    return heat_profile
-
-
-def night_lower_temp(zone_typ, profile_df):
-    night = [22, 23, 24, 1, 2, 3, 4, 5, 6, 7]
-    low = profile_df['Temperaturabsenkung']
-    # todo: add this new function for night lower set temperature
-
-
-def plot_profile(heat_profile, save_plot=False):
-    plt.figure()
-    plt.plot(heat_profile)
-    plt.ylabel('Heat Profile')
-    plt.xlabel('Hours [h]')
-    plt.ylim(ymin=0)
-    plt.xlim(xmin=0)
-    plt.grid()
-    if save_plot:
-        plt.savefig(os.path.join(output_path, 'heat_profile_figure.jpg'))
-    plt.show()
-
-
-if __name__ == "__main__":
-    # calc_total_demand("Verwaltungsgebäude", "mittel", 10000)
-    temperature = pd.read_csv(input_temp_path)['temperature'].values
-    gen_heat_profile("Verwaltungsgebäude", "mittel", 300, temperature,
-                     plot=True)
diff --git a/demand_generator/thermal_demand/SIA2024/temperature.csv b/demand_generator/thermal_demand/SIA2024/temperature.csv
deleted file mode 100644
index df17ec0bd6c7a4ceb496df8c4c3fd91da8d88fe3..0000000000000000000000000000000000000000
--- a/demand_generator/thermal_demand/SIA2024/temperature.csv
+++ /dev/null
@@ -1,8761 +0,0 @@
-timestep,temperature
-1,-0.2
-2,-0.1
-3,0.1
-4,-0.1
-5,-0.1
-6,-0.1
-7,0.1
-8,0.4
-9,0.8
-10,1.3
-11,1.4
-12,2
-13,2.6
-14,2.5
-15,2.4
-16,2.4
-17,2.4
-18,2.5
-19,2.4
-20,2.6
-21,2.5
-22,2.6
-23,3.2
-24,3.4
-25,3.7
-26,3.7
-27,3.8
-28,4
-29,4.1
-30,4.5
-31,4.6
-32,4.5
-33,4.4
-34,3.7
-35,4.4
-36,4.2
-37,3.7
-38,3.8
-39,3.5
-40,2.3
-41,0.9
-42,-0.2
-43,-0.8
-44,-1.2
-45,-1.7
-46,-2.2
-47,-2.5
-48,-2.8
-49,-3.2
-50,-3.4
-51,-3.7
-52,-4
-53,-4.2
-54,-4.2
-55,-4.2
-56,-4.2
-57,-4.2
-58,-3.7
-59,-2.6
-60,-1.6
-61,-1.4
-62,-1
-63,-1
-64,-1.3
-65,-1.7
-66,-2.1
-67,-2.3
-68,-3.1
-69,-3.6
-70,-4.1
-71,-4.6
-72,-5
-73,-5.4
-74,-5.8
-75,-6.3
-76,-6.6
-77,-6.9
-78,-7.1
-79,-7.2
-80,-7.5
-81,-7.8
-82,-7.2
-83,-5.9
-84,-4.1
-85,-2.4
-86,-1.1
-87,-0.7
-88,-1.1
-89,-2.6
-90,-3
-91,-3.5
-92,-4
-93,-4.5
-94,-4.9
-95,-5.1
-96,-5.5
-97,-5.7
-98,-5.8
-99,-5.8
-100,-5.9
-101,-6
-102,-6
-103,-6
-104,-6
-105,-6.1
-106,-5.3
-107,-4.5
-108,-2.8
-109,-1.8
-110,-0.6
-111,-0.3
-112,-0.5
-113,-0.7
-114,-0.5
-115,-0.4
-116,-0.3
-117,0.2
-118,0.4
-119,0.5
-120,0.7
-121,1
-122,1.2
-123,1.3
-124,1.5
-125,1.5
-126,1.4
-127,1.5
-128,1.7
-129,1.9
-130,1.7
-131,1.8
-132,2.1
-133,2.3
-134,2.7
-135,3
-136,3.5
-137,3.4
-138,3.4
-139,2.8
-140,3.2
-141,2.8
-142,3
-143,3.4
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-154,2.5
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-158,4.5
-159,5
-160,4.6
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-162,4.1
-163,3.7
-164,3
-165,3.5
-166,3.5
-167,2.9
-168,2.8
-169,2.6
-170,2.7
-171,2.8
-172,2.6
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-174,2.2
-175,2.7
-176,2.3
-177,2.1
-178,2.3
-179,2.7
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-181,2.9
-182,2.9
-183,2.8
-184,2.7
-185,2.6
-186,2.6
-187,2.6
-188,2.1
-189,1.9
-190,1.4
-191,1.2
-192,0.8
-193,0.5
-194,0
-195,-0.3
-196,-0.3
-197,-0.2
-198,-0.1
-199,-0.1
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-201,0.2
-202,0.4
-203,0.5
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-466,4.7
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-477,8.2
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-487,7.3
-488,7
-489,6.9
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-1603,9.9
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-1617,8.2
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-1739,6.6
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-1743,7.4
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-1745,7.6
-1746,7.3
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-1748,8
-1749,8
-1750,7.8
-1751,7.8
-1752,7.9
-1753,7.9
-1754,8.1
-1755,8.2
-1756,8.4
-1757,8.5
-1758,8.6
-1759,8.5
-1760,8.3
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-1764,6.8
-1765,6.4
-1766,6.5
-1767,6.7
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-1769,6.6
-1770,6.8
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-1776,4.9
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-1784,4.5
-1785,4.3
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-1787,5
-1788,5.1
-1789,5.5
-1790,5.1
-1791,4.7
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-1793,5.8
-1794,5.2
-1795,4.1
-1796,6.1
-1797,6.9
-1798,7.2
-1799,7.1
-1800,6.6
-1801,6.5
-1802,6.3
-1803,6.3
-1804,5.7
-1805,5.5
-1806,5.4
-1807,4.7
-1808,4.9
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-1810,5
-1811,5.6
-1812,5.5
-1813,5.5
-1814,5.6
-1815,5.6
-1816,5.8
-1817,5.8
-1818,6
-1819,6
-1820,5.9
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-1822,6.8
-1823,6.9
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-1825,7.4
-1826,7.4
-1827,7.2
-1828,7.3
-1829,7.2
-1830,7.3
-1831,7.3
-1832,7
-1833,6.9
-1834,6.7
-1835,6.7
-1836,6.9
-1837,7.2
-1838,6.9
-1839,6.7
-1840,6.6
-1841,6.4
-1842,6.3
-1843,6.1
-1844,6.2
-1845,6
-1846,6.3
-1847,6.2
-1848,6.4
-1849,6.3
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-1851,6.3
-1852,6.2
-1853,5.8
-1854,5.7
-1855,6.1
-1856,6.3
-1857,6.2
-1858,6.3
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-1860,5.8
-1861,5.7
-1862,5.4
-1863,5.3
-1864,5.8
-1865,6
-1866,6.2
-1867,6.2
-1868,6.3
-1869,6
-1870,5.7
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-1872,4.6
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-1874,3.2
-1875,3.1
-1876,2.7
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-1881,3.3
-1882,4.6
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-1885,5.6
-1886,5.6
-1887,5.8
-1888,5.9
-1889,6
-1890,5.5
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-1893,4.8
-1894,4.2
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-1896,4.5
-1897,4.1
-1898,3.8
-1899,3.5
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-1902,4.5
-1903,4.7
-1904,4.9
-1905,5.3
-1906,5.4
-1907,6
-1908,6.3
-1909,6.1
-1910,6
-1911,6.7
-1912,6.9
-1913,7.2
-1914,7.1
-1915,7.1
-1916,6.6
-1917,5.6
-1918,5.6
-1919,5.4
-1920,5.4
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-1922,5.2
-1923,5.5
-1924,5.6
-1925,5.6
-1926,5.4
-1927,5.2
-1928,5
-1929,5.4
-1930,6
-1931,6.1
-1932,6.6
-1933,7.5
-1934,8.1
-1935,7.9
-1936,7.5
-1937,7.6
-1938,7.8
-1939,7.5
-1940,7.3
-1941,7.2
-1942,7.1
-1943,7.1
-1944,7.3
-1945,7.8
-1946,7.5
-1947,7.5
-1948,7
-1949,6.5
-1950,6.3
-1951,5.8
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-1953,6.8
-1954,7.9
-1955,8.7
-1956,8.9
-1957,9
-1958,8.9
-1959,9.1
-1960,8.7
-1961,9.3
-1962,9.5
-1963,8.4
-1964,8.9
-1965,7.7
-1966,7
-1967,7
-1968,6.9
-1969,6.6
-1970,6.2
-1971,6.1
-1972,5.6
-1973,5.7
-1974,5.7
-1975,4
-1976,4
-1977,4.3
-1978,5.1
-1979,5.7
-1980,6.7
-1981,7.4
-1982,8.1
-1983,8.7
-1984,9
-1985,9.3
-1986,8.9
-1987,8.4
-1988,7.3
-1989,6.1
-1990,5.6
-1991,5.5
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-1993,4.8
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-1995,4.8
-1996,4.3
-1997,3.8
-1998,3.4
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-2002,6.3
-2003,7.3
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-2005,8.5
-2006,8.6
-2007,9
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-2009,7.3
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-2011,6.4
-2012,6.3
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-2023,6.8
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-2386,8
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-2404,7.2
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-2412,6.2
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-2415,6.2
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-2418,6.7
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-2422,6.3
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-2424,6.2
-2425,6.2
-2426,6.2
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-2431,6
-2432,6.2
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-2437,7.3
-2438,6.8
-2439,7
-2440,6.9
-2441,7.9
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-2444,6
-2445,6
-2446,5.9
-2447,5.5
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-2452,3
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-2454,3.4
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-2460,7.3
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-2464,6.8
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-2467,6.5
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-2472,7
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-2477,7.6
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\ No newline at end of file
diff --git a/demand_generator/thermal_demand/ThermalDemand.py b/demand_generator/thermal_demand/ThermalDemand.py
deleted file mode 100644
index 6b6768b5208e6b091cc913ae0657a8b66a3623a1..0000000000000000000000000000000000000000
--- a/demand_generator/thermal_demand/ThermalDemand.py
+++ /dev/null
@@ -1,288 +0,0 @@
-"""
-MIT License
-
-Copyright (c) 2023 RWTH Aachen University
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
-"""
-
-from math import ceil
-import numpy as np
-import os
-import pandas as pd
-
-# This is needed if we provide a default temperature pattern
-'''
-path = os.path.dirname(os.path.abspath(__file__))
-for j in range(3):
-    path = os.path.dirname(path)
-temperature = pd.read_csv(os.path.join(path, 'main', 'input_files', 'example_data.csv'))['temperature']
-'''
-
-
-def add_weekdays2df(time_df, holidays=None, holiday_is_sunday=False):
-    r"""Giving back a DataFrame containing weekdays and optionally holidays for
-     the given year.
-    Parameters
-    ----------
-    time_df : pandas DataFrame
-        DataFrame to which the weekdays should be added
-    Optional Parameters
-    -------------------
-    holidays : array with information for every hour of the year, if holiday or
-        not (0: holiday, 1: no holiday)
-    holiday_is_sunday : boolean
-        If set to True, all holidays (0) will be set to sundays (7).
-    Returns
-    -------
-    pandas.DataFrame : DataFrame with weekdays
-    Notes
-    -----
-    Using Pandas > 0.16
-    """
-    time_df['weekday'] = time_df.index.weekday + 1
-    time_df['date'] = time_df.index.date
-
-    # Set weekday to Holiday (0) for all holidays
-    if holidays is not None:
-        if isinstance(holidays, dict):
-            holidays = list(holidays.keys())
-        time_df['weekday'].mask(pd.to_datetime(time_df['date']).isin(
-            pd.to_datetime(holidays)), 0, True)
-
-    if holiday_is_sunday:
-        time_df.weekday.mask(time_df.weekday == 0, 7, True)
-
-    return time_df
-
-
-class ThermalDemand:
-    """
-    Parameters
-    ----------
-    year : int
-        year for which the profile is created
-    Attributes
-    ----------
-    datapath : string
-        path to the bdew basic data files (csv)
-    temperature : pandas.Series
-        Series containing hourly temperature data
-    annual_heat_demand : float
-        annual heat demand of building in kWh
-    building_class: int
-        class of building according to bdew classification
-        possible numbers are: 1 - 11
-    shlp_type : string
-        type of standardized heat load profile according to bdew
-        possible types are:
-        GMF, GPD, GHD, GWA, GGB, EFH, GKO, MFH, GBD, GBA, GMK, GBH, GGA, GHA
-    wind_class : int
-        wind classification for building location (0=not windy or 1=windy)
-    ww_incl : boolean
-        decider whether warm water load is included in the heat load profile
-    """
-
-    def __init__(self, df_index, **kwargs):
-        file_dir = os.path.dirname(os.path.abspath(__file__))
-        for j in range(1):
-            file_dir = os.path.dirname(file_dir)
-        self.datapath = os.path.join(file_dir, 'bdew_data')
-        self.df = pd.DataFrame(index=df_index)
-        self.df = add_weekdays2df(self.df, holiday_is_sunday=True,
-                                  holidays=kwargs.get('holidays'))
-        self.df['hour'] = self.df.index.hour + 1
-        self.temperature = kwargs.get('temperature')
-        self.annual_heat_demand = kwargs.get('annual_heat_demand')
-        self.shlp_type = kwargs.get('shlp_type').upper()
-        self.wind_class = kwargs.get('wind_class')
-        self.building_class = kwargs.get('building_class', 0)
-        self.ww_incl = kwargs.get('ww_incl', True)
-        self.name = kwargs.get('name', self.shlp_type)
-
-    def weighted_temperature(self, how='geometric_series'):
-        r"""
-        A new temperature vector is generated containing a multi-day
-        average temperature as needed in the load profile function.
-        Parameters
-        ----------
-        how : string
-            string which type to return ("geometric_series" or "mean")
-        Notes
-        -----
-        Equation for the mathematical series of the average
-        temperature [1]_:
-        .. math::
-            T=\frac{T_{D}+0.5\cdot T_{D-1}+0.25\cdot T_{D-2}+
-                    0.125\cdot T_{D-3}}{1+0.5+0.25+0.125}
-        with :math:`T_D` = Average temperature on the present day
-             :math:`T_{D-i}` = Average temperature on the day - i
-        References
-        ----------
-        .. [1] `BDEW <https://www.avacon.de/cps/rde/xbcr/avacon/15-06-30_Leitfaden_Abwicklung_SLP_Gas.pdf>`_,
-            BDEW Documentation for heat profiles.
-        """
-
-        # calculate daily mean temperature
-        temperature = self.df['temperature'].resample('D').mean().reindex(
-            self.df.index).fillna(method='ffill').fillna(method='bfill')
-
-        if how == 'geometric_series':
-            temperature_mean = (temperature + 0.5 * np.roll(temperature, 24) +
-                                0.25 * np.roll(temperature, 48) +
-                                0.125 * np.roll(temperature, 72)) / 1.875
-        elif how == 'mean':
-            temperature_mean = temperature
-        else:
-            temperature_mean = None
-
-        return temperature_mean
-
-    def get_temperature_interval(self):
-        """Appoints the corresponding temperature interval to each temperature
-        in the temperature vector.
-        """
-        intervals = ({
-            -20: 1, -19: 1, -18: 1, -17: 1, -16: 1, -15: 1, -14: 2,
-            -13: 2, -12: 2, -11: 2, -10: 2, -9: 3, -8: 3, -7: 3, -6: 3, -5: 3,
-            -4: 4, -3: 4, -2: 4, -1: 4, 0: 4, 1: 5, 2: 5, 3: 5, 4: 5, 5: 5,
-            6: 6, 7: 6, 8: 6, 9: 6, 10: 6, 11: 7, 12: 7, 13: 7, 14: 7, 15: 7,
-            16: 8, 17: 8, 18: 8, 19: 8, 20: 8, 21: 9, 22: 9, 23: 9, 24: 9,
-            25: 9, 26: 10, 27: 10, 28: 10, 29: 10, 30: 10, 31: 10, 32: 10,
-            33: 10, 34: 10, 35: 10, 36: 10, 37: 10, 38: 10, 39: 10, 40: 10})
-
-        temperature_rounded = [ceil(i) for i in self.df['temperature_geo']]
-
-        temperature_interval = [intervals[i] for i in temperature_rounded]
-
-        return np.transpose(np.array(temperature_interval))
-
-    def get_sf_values(self, filename='shlp_hour_factors.csv'):
-        """ Determine the h-values
-        Parameters
-        ----------
-        filename : string
-            name of file where sigmoid factors are stored
-        """
-        file = os.path.join(self.datapath, filename)
-        hour_factors = pd.read_csv(file, index_col=0)
-        hour_factors = hour_factors.query(
-            'building_class=={0} and shlp_type=="{1}"'.format(
-                self.building_class, self.shlp_type))
-
-        # Join the two DataFrames on the columns 'hour' and 'hour_of_the_day'
-        # or ['hour' 'weekday'] and ['hour_of_the_day', 'weekday'] if it is
-        # not a residential slp.
-        residential = self.building_class > 0
-        left_cols = ['hour_of_day'] + (['weekday'] if not residential else [])
-        right_cols = ['hour'] + (['weekday'] if not residential else [])
-        sf_mat = pd.DataFrame.merge(
-            hour_factors, self.df, left_on=left_cols, right_on=right_cols,
-            how='outer').sort_index()
-
-        # drop unnecessary columns
-        drop_cols = (
-                ['hour_of_day', 'hour', 'building_class', 'shlp_type',
-                 'date', 'temperature']
-                + (['weekday_x'] if residential else [])
-                + (['weekday_y'] if residential else [])
-                + (['weekday'] if not residential else []))
-
-        sf_mat = sf_mat.drop(drop_cols, 1)
-
-        # Determine the h values
-        length = len(self.temperature)
-        sf = (np.array(sf_mat)[np.array(list(range(0, length)))[:],
-                               (self.get_temperature_interval() - 1)[:]])
-        return np.array(list(map(float, sf[:])))
-
-    def get_sigmoid_parameters(self, filename='shlp_sigmoid_factors.csv'):
-        """ Retrieve the sigmoid parameters from csv-files
-        Parameters
-        ----------
-        filename : string
-            name of file where sigmoid factors are stored
-        """
-
-        file = os.path.join(self.datapath, filename)
-        sigmoid = pd.read_csv(file, index_col=0)
-        sigmoid = sigmoid.query(
-            'building_class=={0} and '.format(self.building_class) +
-            'shlp_type=="{0}" and '.format(self.shlp_type) +
-            'wind_impact=={0}'.format(self.wind_class))
-
-        a = float(sigmoid['parameter_a'])
-        b = float(sigmoid['parameter_b'])
-        c = float(sigmoid['parameter_c'])
-        if self.ww_incl:
-            d = float(sigmoid['parameter_d'])
-        else:
-            d = 0
-        return a, b, c, d
-
-    def get_weekday_parameters(self, filename='shlp_weekday_factors.csv'):
-        """ Retrieve the weekday parameter from csv-file
-        Parameters
-        ----------
-        filename : string
-            name of file where sigmoid factors are stored
-        """
-        file = os.path.join(self.datapath, filename)
-        f_df = pd.read_csv(file, index_col=0)
-
-        tmp_df = f_df.query('shlp_type=="{0}"'.format(self.shlp_type)).drop(
-            'shlp_type', axis=1)
-
-        tmp_df['weekdays'] = np.array(list(range(7))) + 1
-
-        return np.array(list(map(float, pd.DataFrame.merge(
-            tmp_df, self.df, left_on='weekdays', right_on='weekday',
-            how='inner').sort_index()['wochentagsfaktor'])))
-
-    def get_bdew_profile(self):
-        """ Calculation of the hourly heat demand using the bdew-equations
-        """
-        return self.get_normalized_bdew_profile() * self.annual_heat_demand
-
-    def get_normalized_bdew_profile(self):
-        """ Calculation of the normalized hourly heat demand
-        """
-        self.df['temperature'] = self.temperature.values
-        self.df['temperature_geo'] = self.weighted_temperature(
-            how='geometric_series')
-
-        sf = self.get_sf_values()
-
-        [a, b, c, d] = self.get_sigmoid_parameters()
-
-        f = self.get_weekday_parameters()
-        h = (a / (1 + (b / (self.df['temperature_geo'] - 40)) ** c) + d)
-        if self.ww_incl:
-            h = h - a / (1 + (b / (self.df['temperature_geo'] - 40)) ** c)
-        kw = 1.0 / (sum(h * f) / 24)
-        heat_profile_normalized = (kw * h * f * sf)
-
-        return heat_profile_normalized
-
-    def get_test_profile(self):
-        """For testing"""
-        normalized_bdew_profile = self.get_normalized_bdew_profile()
-        normalized_bdew_profile.iloc[:] = [1] * 8760
-        print(normalized_bdew_profile)
-        return normalized_bdew_profile
diff --git a/demand_generator/thermal_demand/__init__.py b/demand_generator/thermal_demand/__init__.py
deleted file mode 100644
index 263bcf570e33ee8d90d71c7e3dfd6c22e84bf83e..0000000000000000000000000000000000000000
--- a/demand_generator/thermal_demand/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .ThermalDemand import ThermalDemand
diff --git a/input_profile_processor/calc_irradiance.py b/input_profile_processor/calc_irradiance.py
deleted file mode 100644
index 4a2fa2d9a078d6289222693d3f609742c9b868cf..0000000000000000000000000000000000000000
--- a/input_profile_processor/calc_irradiance.py
+++ /dev/null
@@ -1,126 +0,0 @@
-"""
-MIT License
-
-Copyright (c) 2023 RWTH Aachen University
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
-"""
-
-import numpy as np
-import pandas as pd
-
-def calc_total_irradiance(irradiance, timer, beta, psi_f, phi, lambda_st, lambda_1, alpha_albedo=0.2, reflection=1, refraction_index=2):
-    """
-    check value: alpha_albedo: ground reflectivity
-    Reflection = 1?
-    Refraction Index = 2?
-
-    beta = plant_parameters[(self.name, self.type)]['beta']
-    psi_f = plant_parameters[(self.name, self.type)]['psi_f']
-    phi = plant_parameters[(self.name, self.type)]['phi']
-    lambda_st = plant_parameters[(self.name, self.type)]['lambda_st']
-    lambda_1 = plant_parameters[(self.name, self.type)]['lambda_1']
-    """
-
-    def calc_direct_beam_incidence_factor():
-        """
-        Angle calculation taken from @Erstellung eines Modells zur Simulation der Solarstrahlung auf beliebig
-        orientierte Flaechen und deren Trennung in Diffus- und Direktanteil@ Peter Ritzenhoff, March 1992
-
-        :param timer: simulation time in TimeStamp format, it will be transformed to seconds of the year
-        :param lambda_1: degree of longitude relative to Greenwich
-        :param lambda_st: degree of longitude of defined time zone
-        :param phi: degree of latitude
-        :param psi_f: azimuth of the pv (see pic.2.4)
-        :param beta: surface inclination tilt angle(Neigungswinkel)
-        :return:
-        """
-
-        daysperyear = 365
-        if timer.is_leap_year:
-            daysperyear = 366
-
-        real_timer = (timer.to_pydatetime() - pd.Timestamp(
-            str(timer.year) + "-01-01 00:00:00").to_pydatetime()).total_seconds()
-        # The day number tn, see eq. 2.1
-        tn = np.mod(real_timer / 86400, daysperyear)
-        # The day angle tw in degree, see eq. 2.3
-        tw = np.mod(tn, daysperyear) * 360 / daysperyear
-        # Elliptical description for delta see eq.2.2
-        delta_rad = np.arcsin(0.3978 * np.sin(tw - 80.2 + 1.92 * np.sin(np.deg2rad(tw - 2.8))))
-        # calculate the hour equation zg, see eq.2.6 (hour equation is for Greenwich)
-        zg = -0.128 * np.sin(np.deg2rad(tw - 2.8)) - 0.165 * np.sin(np.deg2rad(2 * tw + 19.7))
-        # calculate local standard time, time of the day
-        lst = real_timer / 3600 - np.floor(tn) * 24
-        # calculate the solar time, see eq.2.7
-        sz = np.mod(lst + (lambda_1 - lambda_st) / 15 + zg, 24)
-        # calculate the hour angle omega, see eq.2.8
-        omega_rad = np.deg2rad(15 * (sz - 12))
-        phi_rad = np.deg2rad(phi)
-        # calculate the sun elevation gamma, see eq.2.9
-        gamma_rad = np.arcsin(
-            np.sin(phi_rad) * np.sin(delta_rad) + np.cos(omega_rad) * np.cos(phi_rad) * np.cos(delta_rad))
-        # calculate the sun azimuth, see eq.2.10
-        psi_s_rad = np.arccos(
-            (np.sin(phi_rad) * np.sin(gamma_rad) - np.sin(delta_rad)) / (np.cos(phi_rad) * np.cos(gamma_rad)))
-        psi_s_rad = -psi_s_rad if 0 <= sz < 12 else psi_s_rad
-        # calculate the Surface(PV)-sun-azimuth, see eq.11
-        psi_fs_rad = np.deg2rad(np.rad2deg(psi_s_rad) - psi_f)
-        beta_rad_local = np.deg2rad(beta)
-        # calculate the angle of incidence of the sun relative to the surface, see eq.2.12
-        cos_theta = np.cos(gamma_rad) * np.sin(beta_rad_local) * np.cos(psi_fs_rad) + np.sin(gamma_rad) * np.cos(
-            beta_rad_local)
-        theta_local = np.rad2deg(np.arccos(cos_theta))
-        # perhaps a good method, DNI will be considered once the sun height reaches 4 degrees
-        f_beam_local = max(cos_theta, 0)
-        return theta_local, f_beam_local
-
-    # g_total = g_diffuse + g_reflected + g_beam
-    beta_deg = beta
-    beta_rad = np.deg2rad(beta_deg)  # tilt angle in [rad]
-    R_d = (1 + np.cos(beta_rad)) / 2
-    R = alpha_albedo * (1 - np.cos(beta_rad)) / 2  # alpha_albedo: ground reflectivity
-    beta_table = [-0.167716, 4.91582, 9.99807, 14.8933, 19.9769, 25.0591, 30.1427, 35.0379, 39.5553, 44.6389,
-                  49.7224, 54.6189, 59.892, 64.4095, 69.6826, 74.5791, 79.4744, 84.5605, 89.644]
-    diffuse_reflection_loss_table = [0.94538, 0.945765, 0.949564, 0.949935, 0.950321, 0.954119, 0.954505, 0.954876,
-                                     0.958631, 0.959017, 0.959403, 0.956361, 0.953348, 0.957103, 0.95409, 0.951049,
-                                     0.95142, 0.94498, 0.945365]
-    diffuse_reflection_loss = np.interp(beta_deg, beta_table, diffuse_reflection_loss_table)
-    theta, f_beam = calc_direct_beam_incidence_factor()
-    theta_out = np.rad2deg(np.arcsin(np.sin(np.deg2rad(theta / refraction_index))))
-    r_ges = (np.tan(theta - theta_out) ** 2 / np.tan(theta + theta_out) ** 2) * 0.5 * (np.cos(theta) ** 2 + 1) + \
-            (np.sin(theta - theta_out) ** 2 / np.sin(theta + theta_out) ** 2) * 0.5 * np.sin(theta) ** 2
-    transmission_factor = max(0, 1 - r_ges)
-    if reflection == 0:
-        diffuse_reflection_loss = 1
-        transmission_factor = 1
-    g_diffuse = irradiance[0] * R_d * diffuse_reflection_loss
-    g_reflected = irradiance[1] * R
-    g_beam = irradiance[2] * f_beam * transmission_factor
-
-    g_total = g_diffuse + g_reflected + g_beam
-    return g_total
-
-def generate_g_t_series(irradiance, beta, psi_f, phi, lambda_st, lambda_1, t_start, dynamic):
-    T = list(irradiance.index)
-    g_total_lst = []
-    for t in T:
-        g_total_lst.append(calc_total_irradiance(irradiance.loc[t], t, beta, psi_f, phi, lambda_st, lambda_1))
-
-    return pd.Series(data=g_total_lst, index=T)
diff --git a/input_profile_processor/input_profile_processor.py b/input_profile_processor/input_profile_processor.py
deleted file mode 100644
index e4f96db7e8aada0beab15d1518ffaeda76db77ea..0000000000000000000000000000000000000000
--- a/input_profile_processor/input_profile_processor.py
+++ /dev/null
@@ -1,247 +0,0 @@
-"""
-MIT License
-
-Copyright (c) 2023 RWTH Aachen University
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
-"""
-
-from Model_Library.dynamics import Profile
-from Tooling.demand_generator.electrical_demand.ElectricalDemand import ElectricalDemand
-from Tooling.demand_generator.thermal_demand.ThermalDemand import ThermalDemand
-from Tooling.input_profile_processor.calc_irradiance import generate_g_t_series
-from Tooling.modifier import Modifier
-
-from datetime import datetime, timedelta
-import numpy as np
-import pandas as pd
-
-def process_input_profiles(input_profile_dict, t_start, dynamic):
-    d_step_min = np.min(dynamic.step_lengths()) / 3600.0
-    # This entire process assumes four things:
-    # 1. d_step_min devides a day into an integer amount of steps
-    # 2. d_step_min devides the intervall from YYYY.MM.DD 00:00:00 of the day containing t_start to t_start into an integer amount of steps
-    # 3. The timestamps of the desired timeseries either line up the with timestamps provided by the profiles in the files or the other way around
-    # 4. The sizes of all time steps in the given dynamic are devisible by 
-    input_profiles = {}
-    for input_profile_name, input_profile_config in input_profile_dict.items():
-        if 'file' in input_profile_config:
-            profile = load_profile(input_profile_name, input_profile_config['file'], t_start, dynamic, d_step_min)
-        elif 'generate' in input_profile_config:
-            profile = generate_profile(input_profile_name, input_profile_config['type'], input_profile_config['generate'], input_profiles, t_start, dynamic, d_step_min)
-        elif 'modify' in input_profile_config:
-            profile = modify_profile(input_profile_name, input_profile_config['type'], input_profile_config['modify'], input_profiles, t_start, dynamic, d_step_min)
-        input_profiles[input_profile_name] = (input_profile_config['type'], profile)
-
-    for input_profile_name, (input_profile_type, input_profile) in input_profiles.items():
-        input_profile.drop(pd.date_range(input_profile.index[0], t_start - timedelta(hours = d_step_min), freq = str(d_step_min) + 'H'), inplace = True)
-        input_profile.drop(pd.date_range(t_start + pd.Timedelta(hours = np.sum(dynamic.step_lengths()) / 3600.0), input_profile.index[-1], freq = str(d_step_min) + 'H'), inplace = True)
-
-        if len(input_profile) != dynamic.number_of_steps():
-            index = 0
-            drop = []
-            for index in dynamic.time_steps():
-                if dynamic.step_size(index) != d_step_min:
-                    number_of_steps = int(dynamic.step_size(index) / d_step_min)
-                    input_profile.iloc[index] = sum(d_step_min * input_profile.iloc[index + i] for i in range(number_of_steps)) / dynamic.step_size(index)
-                    drop.extend(index + i + 1 for i in range(number_of_steps - 1))
-                    index += number_of_steps
-                else:
-                    index += 1
-            input_profile.drop((input_profile.index[i] for i in drop), inplace = True)
-
-        if input_profile_type == 'irradiance':
-            lambda_1 = 14.122
-            lambda_st = 15
-            phi = 52.21
-            psi_f = 0
-            beta = 30
-            input_profile = generate_g_t_series(input_profile, beta, psi_f, phi, lambda_st, lambda_1, t_start, dynamic)
-
-        input_profile = input_profile.squeeze()
-        input_profile.set_axis(list(range(dynamic.number_of_steps())), inplace = True)
-        if isinstance(input_profile, pd.Series):
-            input_profiles[input_profile_name] = Profile(input_profile.values, dynamic)
-        else:
-            input_profiles[input_profile_name] = Profile(input_profile, dynamic)
-    return input_profiles
-
-def resample_profile(name, profile, t_start, dynamic, d_step_min, t_last):
-    profile = profile.resample(str(d_step_min) + 'H').mean().interpolate('linear')
-    if t_start < profile.index[0]:
-        missing_indices = pd.date_range(t_start, profile.index[0] - timedelta(hours = d_step_min), freq = str(d_step_min) + 'H')
-        print(f'For input profile {name} {len(missing_indices)} {"values are" if len(missing_indices) > 1 else "value is"} extrapolated to the beginning of the profile!')
-        profile = pd.concat([pd.DataFrame([profile.iloc[0]], index = missing_indices), profile])
-    if profile.index[-1] < t_last:
-        missing_indices = pd.date_range(profile.index[-1] + timedelta(hours = d_step_min), t_last, freq = str(d_step_min) + 'H')
-        print(f'For input profile {name} {len(missing_indices)} {"values are" if len(missing_indices) > 1 else "value is"} extrapolated to the end of the profile!')
-        profile = pd.concat([profile, pd.DataFrame([profile.iloc[-1]], index = missing_indices)])
-    return profile
-
-def expand_profile_to_year(profile, year, t_step):
-    if datetime(year, 1, 1, 0) < profile.index[0]:
-        missing_indices = pd.date_range(datetime(year, 1, 1, 0), profile.index[0] - timedelta(hours = t_step), freq = str(t_step) + 'H')
-        profile = pd.concat([pd.DataFrame([profile.iloc[0]], index  =missing_indices), profile])
-    if profile.index[-1] < datetime(year + 1, 1, 1, 0) - timedelta(hours = t_step):
-        missing_indices = pd.date_range(profile.index[-1] + timedelta(hours = t_step), datetime(year + 1, 1, 1, 0) - timedelta(hours = t_step), freq = str(t_step) + 'H')
-        profile = pd.concat([profile, pd.DataFrame([profile.iloc[-1]], index = missing_indices)])
-    return profile
-
-def load_profile(name, file, t_start, dynamic, d_step_min):
-    t_last = t_start + pd.Timedelta(hours = np.sum(dynamic.step_lengths()[:-1]) / 3600.0)
-    profile = pd.read_csv(file, index_col = 0)
-    try:
-        file_start = pd.to_datetime(profile.index[0], format = '%d-%m-%Y %H:%M:%S')
-        format = '%d-%m-%Y %H:%M:%S'
-    except ValueError:
-        format = '%Y-%m-%d %H:%M:%S'
-    # left_index = 0
-    # right_index = len(profile) - 1
-    # middle_index = left_index + int((right_index - left_index) / 2)
-    # middle_time = pd.to_datetime(profile.index[middle_index], format = format)
-    # while True:
-    #     if right_index - left_index == 1:
-    #         # We now know that t_start is strictly larger than the time at left_index and smaller or equal than the time at right_index
-    #         # So we have to test if t_start is equal to the time at right_index and return right_index if this is the case
-    #         # Otherwise return left_index
-    #         right_time = pd.to_datetime(profile.index[right_index], format = format)
-    #         if right_time <= t_start:
-    #             first_index = right_index
-    #         else:
-    #             first_index = left_index
-    #         break
-    #     if middle_time < t_start:
-    #         left_index = middle_index
-    #     else:
-    #         right_index = middle_index
-    #     middle_index = left_index + int((right_index - left_index) / 2)
-    #     middle_time = pd.to_datetime(profile.index[middle_index], format = format)
-    # left_index = 0
-    # right_index = len(profile) - 1
-    # middle_index = left_index + int((right_index - left_index) / 2)
-    # middle_time = pd.to_datetime(profile.index[middle_index], format = format)
-    # while True:
-    #     if right_index - left_index == 1:
-    #         # We now know that t_last is lager or equal than the time at left_index and strictly smaller than the time at right_index
-    #         # So we have to test if t_last is equal to the time at left_index and return left_index if this is the case
-    #         # Otherwise return right_index
-    #         left_time = pd.to_datetime(profile.index[left_index], format = format)
-    #         if t_last <= left_time:
-    #             last_index = left_index
-    #         else:
-    #             last_index = right_index
-    #         break
-    #     if t_last < middle_time:
-    #         right_index = middle_index
-    #     else:
-    #         left_index = middle_index
-    #     middle_index = left_index + int((right_index - left_index) / 2)
-    #     middle_time = pd.to_datetime(profile.index[middle_index], format = format)
-    # first_index = 0
-    # last_index = len(profile) - 1
-    # profile = profile[first_index:last_index + 1]
-    profile.index = pd.to_datetime(profile.index, format = format)
-    return resample_profile(name, profile, t_start, dynamic, d_step_min, t_last)
-
-def generate_profile(name, profile_type, parameters, input_profiles, t_start, dynamic, d_step_min):
-    t_last = t_start + pd.Timedelta(hours = np.sum(dynamic.step_lengths()[:-1]) / 3600.0)
-    years = range(t_start.year, t_last.year + 1)
-
-    if profile_type == 'electricity_demand':
-        profiles = []
-        for year in years:
-            year_profile = ElectricalDemand(year).get_profile(parameters['yearly_demand'], 'h0', True)  # True: with smoothing function for household profiles, False: no smoothing function for household profiles
-            profiles.append(year_profile)
-        profile = pd.concat(profiles)
-        return resample_profile(name, profile, t_start, dynamic, d_step_min, t_last)
-    elif profile_type == 'therm_demand':
-        profiles = []
-        for year in years:
-            demand_df = pd.DataFrame(index=pd.date_range(datetime(year, 1, 1, 0),
-                                                         periods=8760 / d_step_min,
-                                                         freq=str(d_step_min) + 'H'))
-            # Fixme: non-residential building only have building_class = 0
-            # residential building could varies from 1 to 11
-            # same problem for hot water demand.
-            # temporary fix
-            if len(input_profiles[parameters['temperature']][1]) < len(demand_df.index):
-                temp = list(input_profiles[parameters['temperature']])
-                temp[1] = expand_profile_to_year(input_profiles[parameters['temperature']][1], year, d_step_min)
-                input_profiles[parameters['temperature']] = tuple(temp)
-            year_profile = ThermalDemand(demand_df.index,
-                                         shlp_type='EFH',
-                                         temperature=input_profiles[parameters['temperature']][1],
-                                         building_class=1,
-                                         wind_class=1,
-                                         annual_heat_demand=parameters['yearly_demand'],
-                                         name='HeatDemand_EFH',
-                                         ww_incl=0).get_bdew_profile()
-            profiles.append(year_profile)
-        profile = pd.concat(profiles)
-        return profile
-    elif profile_type == 'hot_water_demand':
-        profiles = []
-        for year in years:
-            demand_df = pd.DataFrame(index=pd.date_range(datetime(year, 1, 1, 0),
-                                                         periods=8760 / d_step_min,
-                                                         freq=str(d_step_min) + 'H'))
-            # temporary fix
-            if len(input_profiles[parameters['temperature']][1]) < len(demand_df.index):
-                temp = list(input_profiles[parameters['temperature']])
-                temp[1] = expand_profile_to_year(input_profiles[parameters['temperature']][1], year, d_step_min)
-                input_profiles[parameters['temperature']] = tuple(temp)
-            year_profile = ThermalDemand(demand_df.index,
-                                         shlp_type='EFH',
-                                         temperature=input_profiles[parameters['temperature']][1],
-                                         building_class=1,
-                                         wind_class=1,
-                                         annual_heat_demand=parameters['yearly_demand'],
-                                         name='DomesticHotWaterDemand_EFH',
-                                         ww_incl=1).get_bdew_profile()
-            profiles.append(year_profile)
-        profile = pd.concat(profiles)
-        return profile
-    else:
-        raise Exception("Generator for profile type " + str(profile_type) + " is not implemented!")
-
-
-def modify_profile(name, mod_type: str, parameters, input_profiles, t_start, dynamic, d_step_min):
-    # Example of the parameters for use in the runme
-    # ------------------------------------------------------------------------------------------------------------------
-    # 'temperature_1': ['prophet', 'modify', 'input_files/data/temperature/temperature.csv', 'temperature',
-    #                       {"winter1": 0, "spring": 0, "summer": 0,"fall": 0,"winter2": 0},
-    #                       "Tooling/Modifier/prophet_models/serialized_model_08032023.json", True, True]
-    # ------------------------------------------------------------------------------------------------------------------
-
-    if mod_type == "prophet":
-        # t_start : timestamp, e.g.: pd.Timestamp("2021-01-01 00:00:00")
-        # t_horizon: int, number of days that should be created, for now hardcoded to 365 due to external dependencies
-        # parameters[]: array of additional parameters, should contain csv_path, season_offset, model_path, new, save.
-        # For details on the specific parameters look at the documentation of the prophet_based_modification
-        profile = Modifier.prophet_based_modification(csv_path = parameters[0],
-                                                      feature_name = parameters[1],
-                                                      season_offset = parameters[2],
-                                                      start = t_start,
-                                                      model_path = parameters[3],
-                                                      new = parameters[4],
-                                                      mod_period = 365,
-                                                      save = parameters[5])
-        return profile
-    else:
-        raise Exception("Modification for mod type " + str(mod_type) + " is not implemented!")