Updated demand_generator.py to work with different profiles

Updated shlp_hour_factors.csv with additional data
Updated shlp.csv with new entries
Updated selp.csv with new entries
Refactored .gitignore and added .vscode and test.py

.gitignore

 __pycache__
+.vscode
+test.py

demand_generator.py

@@ -39,10 +39,15 @@ class ElectricityDemandGenerator:
             os.path.join(os.path.dirname(__file__), "selp.csv"), index_col=[0, 1]
         )
         self.profiles = dict()
+
+        profile_columns = profiles_df.columns
+
+        #loading profiles into self.profiles dictionary, indexed py period, weekday and profile
+        for profile in profile_columns:
             for period in ["winter", "transition", "summer"]:
                 for weekday in ["working_day", "saturday", "sunday"]:
-                self.profiles[period, weekday] = profiles_df.loc[
-                    (period, weekday), "H0"
+                    self.profiles[period, weekday, profile] = profiles_df.loc[
+                        (period, weekday), profile
                     ].values
                           
         # set standart period ranges
@@ -64,11 +69,12 @@ class ElectricityDemandGenerator:
         )
         self.dynamic_factor = factor_function(np.arange(1, 366, dtype=float))
 
-    def generate(self, year, annual_demand):
+    def generate(self, year, annual_demand, profile_type="H0"):
         # year: year to generate the data for in astronomical numbering
         # annual_demand: annual electricity demand in kWh
 
         # days in the year, depending on wehter the given year a leap year
+
         days_in_year = 365 if not calendar.isleap(year) else 366
 
         # generate series of days in the year and to which period they belong
@@ -89,15 +95,15 @@ class ElectricityDemandGenerator:
             weekday = date.weekday()
             if weekday < 5:
                 profile[i * 24 * 4 : (i + 1) * 24 * 4] = (
-                    self.profiles[period, "working_day"] * self.dynamic_factor[i]
+                    self.profiles[period, "working_day", profile_type] * self.dynamic_factor[i]
                 )
             elif weekday == 5:
                 profile[i * 24 * 4 : (i + 1) * 24 * 4] = (
-                    self.profiles[period, "saturday"] * self.dynamic_factor[i]
+                    self.profiles[period, "saturday", profile_type] * self.dynamic_factor[i]
                 )
             else:
                 profile[i * 24 * 4 : (i + 1) * 24 * 4] = (
-                    self.profiles[period, "sunday"] * self.dynamic_factor[i]
+                    self.profiles[period, "sunday", profile_type] * self.dynamic_factor[i]
                 )
 
         # normalize profile and convert to kW
@@ -121,20 +127,32 @@ class HeatingDemandGenerator:
             index_col=[0, 1],
         )
 
-        self.a = profiles_df["A"]["D13"]
-        self.b = profiles_df["B"]["D13"]
-        self.c = profiles_df["C"]["D13"]
-        self.d = profiles_df["D"]["D13"]
+        self.a = profiles_df["A"]
+        self.b = profiles_df["B"]
+        self.c = profiles_df["C"]
+        self.d = profiles_df["D"]
+
+        self.theta_0 = profiles_df["theta_0"]
 
-        self.theta_0 = profiles_df["theta_0"]["D13"]
+        self.weekday_factors = dict()
 
-        self.weekday_factors = profiles_df.loc[
-            "D13", ["F_Mon", "F_Tue", "F_Wed", "F_Thu", "F_Fri", "F_Sat", "F_Sun"]
+        for profile in profiles_df.index:
+            self.weekday_factors[profile] = profiles_df.loc[
+                profile, ["F_Mon", "F_Tue", "F_Wed", "F_Thu", "F_Fri", "F_Sat", "F_Sun"]
             ].values
 
-        self.hour_factors = hour_factors_df.loc["D13"].values
+        # self.weekday_factors = profiles_df.loc[
+        #     "D13", ["F_Mon", "F_Tue", "F_Wed", "F_Thu", "F_Fri", "F_Sat", "F_Sun"]
+        # ].values
         
-    def generate(self, year, annual_demand, temperatures, seperate_warm_water=False):
+        self.hour_factors = dict()
+
+        for profile in hour_factors_df.index.get_level_values(0).unique():
+            self.hour_factors[profile] = hour_factors_df.loc[profile].values
+
+        #self.hour_factors = hour_factors_df.loc["D13"].values
+
+    def generate(self, year, annual_demand, temperatures, seperate_warm_water=False, profile_type="D13"):
         # year: year to generate the data for in astronomical numbering
         # annual_demand: annual heating demand in kWh
         # temperatures: daily temperatures in °C
@@ -154,18 +172,20 @@ class HeatingDemandGenerator:
             + 0.125 * np.roll(temperatures, 3)
         ) / 1.875
 
-        # compute sigmoid function, weeday factors and hour factors
-        temp = self.b / (allocation_temperatures - 40.0)
-        h = (self.a / (1.0 + np.sign(temp) * (np.abs(temp) ** self.c))) + self.d
+        # compute sigmoid function, weekday factors and hour factors
+        temp = self.b[profile_type] / (allocation_temperatures - 40.0)
+        h = (self.a[profile_type] / (1.0 + np.sign(temp) * (np.abs(temp) ** self.c[profile_type]))) + self.d[profile_type]
 
         f_wd = np.array(
             pd.date_range(datetime.datetime(year, 1, 1), periods=days_in_year).map(
-                lambda a: self.weekday_factors[a.weekday()]
+                lambda a: self.weekday_factors[profile_type][a.weekday()]
             ),
             dtype=float,
         )
 
-        f_h = self.hour_factors[
+        hour_factors = self.hour_factors[profile_type] if profile_type in self.hour_factors.keys() else self.hour_factors["D13"]
+
+        f_h = hour_factors[
             (
                 (allocation_temperatures <= -15.0) * 1
                 + (
@@ -196,11 +216,11 @@ class HeatingDemandGenerator:
         ]
 
         # compute reference daily demand (kundenwert)
-        reference_temp = self.b / (np.full(days_in_year, 8.0, dtype=float) - 40.0)
+        reference_temp = self.b[profile_type] / (np.full(days_in_year, 8.0, dtype=float) - 40.0)
         reference_h = (
-            self.a
-            / (1.0 + np.sign(reference_temp) * (np.abs(reference_temp) ** self.c))
-        ) + self.d
+            self.a[profile_type]
+            / (1.0 + np.sign(reference_temp) * (np.abs(reference_temp) ** self.c[profile_type]))
+        ) + self.d[profile_type]
 
         reference_daily_demand = annual_demand / np.sum(reference_h * f_wd)
 
@@ -213,7 +233,7 @@ class HeatingDemandGenerator:
                     reference_daily_demand * h[i] * f_wd[i] * (f_h[i] / 100.0)
                 )
                 profile_warm_water[i * 24 : (i + 1) * 24] = (
-                    reference_daily_demand * self.d * f_wd[i] * (f_h[i] / 100.0)
+                    reference_daily_demand * self.d[profile_type] * f_wd[i] * (f_h[i] / 100.0)
                 )
 
             return profile_sum - profile_warm_water, profile_warm_water

shlp.csv

 code,A,B,C,D,theta_0,F_Mon,F_Tue,F_Wed,F_Thu,F_Fri,F_Sat,F_Sun
 D13,3.0469695,-37.1833141,5.6727847,0.0961931,40.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+D14,3.1850191,-37.4124155,6.1723179,0.0761096,40.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+D15,3.0469695,-37.1833141,5.6727847,0.0961931,40.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+D23,2.3877618,-34.7213605,5.8164304,0.1208194,40.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+D24,2.5187775,-35.0333754,6.2240634,0.1010782,40.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+D25,2.6564406,-35.2516927,6.5182659,0.0812059,40.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+HK3,0.4040932,-24.4392968,6.5718175,0.7107710,40.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+KO5,4.3624833,-38.6634022,7.5974644,0.0083264,40.0,1.0354,1.0523,1.0449,1.0494,0.9885,0.8860,0.9435
+KO4,3.4428943,-36.6590504,7.6083226,0.0746850,40.0,1.0354,1.0523,1.0449,1.0494,0.9885,0.8860,0.9435
+KO3,2.7172288,-35.1412563,7.1303395,0.1418472,40.0,1.0354,1.0523,1.0449,1.0494,0.9885,0.8860,0.9435
+KO2,2.0660501,-33.6016520,6.6753610,0.2309125,40.0,1.0354,1.0523,1.0449,1.0494,0.9885,0.8860,0.9435
+KO1,1.4159571,-30.8425192,6.3467557,0.3211791,40.0,1.0354,1.0523,1.0449,1.0494,0.9885,0.8860,0.9435
+HA5,4.8252376,-39.2802564,8.6240217,0.0099945,40.0,1.0358,1.0232,1.0252,1.0295,1.0253,0.9675,0.8935
+HA4,4.0196902,-37.8282037,8.1593369,0.0472845,40.0,1.0358,1.0232,1.0252,1.0295,1.0253,0.9675,0.8935
+HA3,3.5811214,-36.9650065,7.2256947,0.0448416,40.0,1.0358,1.0232,1.0252,1.0295,1.0253,0.9675,0.8935
+HA2,2.8544749,-35.6294231,7.0058264,0.1164772,40.0,1.0358,1.0232,1.0252,1.0295,1.0253,0.9675,0.8935
+HA1,2.3742828,-34.7595501,5.9987037,0.1494411,40.0,1.0358,1.0232,1.0252,1.0295,1.0253,0.9675,0.8935
+MK5,3.5862355,-37.0802994,8.2420572,0.0146008,40.0,1.0699,1.0365,0.9933,0.9948,1.0659,0.9362,0.9034
+MK4,3.1177248,-35.8715062,7.5186829,0.0343301,40.0,1.0699,1.0365,0.9933,0.9948,1.0659,0.9362,0.9034
+MK3,2.7882424,-34.8806130,6.5951899,0.0540329,40.0,1.0699,1.0365,0.9933,0.9948,1.0659,0.9362,0.9034
+MK2,2.2908184,-33.1476867,6.3714765,0.0810023,40.0,1.0699,1.0365,0.9933,0.9948,1.0659,0.9362,0.9034
+MK1,1.8644534,-30.7071633,6.4626937,0.1048339,40.0,1.0699,1.0365,0.9933,0.9948,1.0659,0.9362,0.9034
+BD5,4.5699506,-38.5353392,7.5976991,0.0066314,40.0,1.1052,1.0857,1.0378,1.0622,1.0266,0.7629,0.9196
+BD4,3.7500000,-37.5000000,6.8000000,0.0609113,40.0,1.1052,1.0857,1.0378,1.0622,1.0266,0.7629,0.9196
+BD3,2.9177027,-36.1794117,5.9265162,0.1151912,40.0,1.1052,1.0857,1.0378,1.0622,1.0266,0.7629,0.9196
+BD2,2.1095878,-35.8444508,5.2154672,0.2854583,40.0,1.1052,1.0857,1.0378,1.0622,1.0266,0.7629,0.9196
+BD1,1.2903505,-35.2349868,2.1064247,0.4557253,40.0,1.1052,1.0857,1.0378,1.0622,1.0266,0.7629,0.9196
+BH5,2.9800000,-35.8000000,5.6340581,0.0000000,40.0,0.9767,1.0389,1.0028,1.0162,1.0024,1.0043,0.9587
+BH4,2.4595181,-35.2532124,6.0587001,0.1647370,40.0,0.9767,1.0389,1.0028,1.0162,1.0024,1.0043,0.9587
+BH3,2.0102472,-35.2532124,6.1544406,0.3294741,40.0,0.9767,1.0389,1.0028,1.0162,1.0024,1.0043,0.9587
+BH2,1.7005279,-35.1500000,6.1632739,0.4298261,40.0,0.9767,1.0389,1.0028,1.0162,1.0024,1.0043,0.9587
+BH1,1.4771786,-35.0834447,5.4123425,0.4744264,40.0,0.9767,1.0389,1.0028,1.0162,1.0024,1.0043,0.9587
+WA5,1.2768854,-34.3424371,5.4518822,0.5572660,40.0,1.2457,1.2615,1.2707,1.2430,1.1276,0.3877,0.4638
+WA4,1.0535875,-35.3000000,4.8662747,0.6811042,40.0,1.2457,1.2615,1.2707,1.2430,1.1276,0.3877,0.4638
+WA3,0.7657290,-36.0237912,4.8662747,0.8049425,40.0,1.2457,1.2615,1.2707,1.2430,1.1276,0.3877,0.4638
+WA2,0.6166229,-38.4000000,3.8705352,0.8700250,40.0,1.2457,1.2615,1.2707,1.2430,1.1276,0.3877,0.4638
+WA1,0.4000000,-40.5149482,2.8747957,0.9351076,40.0,1.2457,1.2615,1.2707,1.2430,1.1276,0.3877,0.4638
+GA5,3.3295575,-36.0146211,8.7767465,0.0000000,40.0,0.9322,0.9894,1.0033,1.0109,1.0180,1.0356,1.0106
+GA4,2.8195656,-36.0000000,7.7368518,0.1572810,40.0,0.9322,0.9894,1.0033,1.0109,1.0180,1.0356,1.0106
+GA3,2.2850165,-36.2878584,6.5885126,0.3150535,40.0,0.9322,0.9894,1.0033,1.0109,1.0180,1.0356,1.0106
+GA2,1.6487623,-36.3992736,6.2149172,0.4877637,40.0,0.9322,0.9894,1.0033,1.0109,1.0180,1.0356,1.0106
+GA1,1.1770345,-39.1599914,4.2076110,0.6604739,40.0,0.9322,0.9894,1.0033,1.0109,1.0180,1.0356,1.0106
+BA5,1.2779567,-34.5173920,5.7212303,0.5457333,40.0,1.0848,1.1211,1.0769,1.1353,1.1402,0.4852,0.9565
+BA4,0.9315889,-33.3500000,5.7212303,0.6656494,40.0,1.0848,1.1211,1.0769,1.1353,1.1402,0.4852,0.9565
+BA3,0.6261962,-33.0000000,5.7212303,0.7855655,40.0,1.0848,1.1211,1.0769,1.1353,1.1402,0.4852,0.9565
+BA2,0.3879191,-35.5000000,4.0000000,0.9054815,40.0,1.0848,1.1211,1.0769,1.1353,1.1402,0.4852,0.9565
+BA1,0.1500000,-36.0000000,2.0000000,1.0000000,40.0,1.0848,1.1211,1.0769,1.1353,1.1402,0.4852,0.9565
+GB5,3.9320532,-38.1433248,7.6185871,0.0230297,40.0,0.9897,0.9627,1.0507,1.0552,1.0297,0.9767,0.9353
+GB4,3.6017736,-37.8825368,6.9836070,0.0548262,40.0,0.9897,0.9627,1.0507,1.0552,1.0297,0.9767,0.9353
+GB3,3.2572742,-37.5000000,6.3462148,0.0866227,40.0,0.9897,0.9627,1.0507,1.0552,1.0297,0.9767,0.9353
+GB2,3.3904645,-39.2875216,4.4905740,0.0834783,40.0,0.9897,0.9627,1.0507,1.0552,1.0297,0.9767,0.9353
+GB1,3.1761945,-40.8366609,3.6785892,0.1502156,40.0,0.9897,0.9627,1.0507,1.0552,1.0297,0.9767,0.9353
+PD5,4.7462814,-38.7504294,10.2753334,0.0000000,40.0,1.0214,1.0866,1.0720,1.0557,1.0117,0.9001,0.8525
+PD4,3.8500000,-37.0000000,10.2405021,0.0469243,40.0,1.0214,1.0866,1.0720,1.0557,1.0117,0.9001,0.8525
+PD3,3.2000000,-35.8000000,8.4000000,0.0938486,40.0,1.0214,1.0866,1.0720,1.0557,1.0117,0.9001,0.8525
+PD2,2.5784173,-34.7321261,6.4805035,0.1407729,40.0,1.0214,1.0866,1.0720,1.0557,1.0117,0.9001,0.8525
+PD1,1.4894022,-32.4252678,8.1732612,0.3905987,40.0,1.0214,1.0866,1.0720,1.0557,1.0117,0.9001,0.8525
+MF5,2.6564406,-35.2516927,6.5182659,0.0812059,40.0,1.0354,1.0523,1.0449,1.0494,0.9885,0.8860,0.9435
+MF4,2.5187775,-35.0333754,6.2240634,0.1010782,40.0,1.0354,1.0523,1.0449,1.0494,0.9885,0.8860,0.9435
+MF3,2.3877618,-34.7213605,5.8164304,0.1208194,40.0,1.0354,1.0523,1.0449,1.0494,0.9885,0.8860,0.9435
+MF2,2.2486333,-34.5428431,5.5545245,0.1408220,40.0,1.0354,1.0523,1.0449,1.0494,0.9885,0.8860,0.9435
+MF1,2.1163531,-34.2628623,5.1763874,0.1606945,40.0,1.0354,1.0523,1.0449,1.0494,0.9885,0.8860,0.9435
+HD4,3.0084346,-36.6078453,7.3211870,0.1549660,40.0,1.0300,1.0300,1.0200,1.0300,1.0100,0.9300,0.9500
+HD3,2.5792510,-35.6816144,6.6857976,0.1995541,40.0,1.0300,1.0300,1.0200,1.0300,1.0100,0.9300,0.9500
\ No newline at end of file

shlp_hour_factors.csv

@@ -9,3 +9,13 @@ D13,6,1.42,1.55,1.67,2.0,2.98,5.41,6.85,6.79,5.78,5.27,4.78,4.35,4.15,4.02,3.99,
 D13,7,0.96,1.05,1.12,1.62,2.89,6.62,8.21,7.57,6.09,6.01,5.13,4.83,4.42,3.97,3.78,3.59,4.0,4.28,4.56,5.13,5.19,4.42,3.09,1.47
 D13,8,0.45,0.54,0.44,0.81,1.91,6.01,9.39,8.09,6.14,5.87,5.33,5.41,5.21,4.2,4.03,3.6,4.23,4.98,4.82,5.08,5.26,4.5,2.62,1.08
 D13,9,0.45,0.54,0.44,0.81,1.91,6.01,9.39,8.09,6.14,5.87,5.33,5.41,5.21,4.2,4.03,3.6,4.23,4.98,4.82,5.08,5.26,4.5,2.62,1.08
+D23,0,2.99,2.96,2.94,3.04,3.55,5.43,5.49,4.59,4.59,4.47,4.23,4.36,4.27,4.23,4.19,4.30,4.36,4.58,4.53,4.63,4.55,4.32,4.17,3.21
+D23,1,2.98,2.94,2.92,3.02,3.55,5.43,5.25,4.62,4.61,4.49,4.25,4.38,4.29,4.24,4.21,4.32,4.38,4.60,4.57,4.66,4.58,4.34,4.18,3.19
+D23,2,2.91,2.88,2.85,2.96,3.52,5.50,5.27,4.65,4.63,4.51,4.26,4.39,4.30,4.24,4.21,4.32,4.39,4.62,4.58,4.69,4.61,4.35,4.19,3.15
+D23,3,2.58,2.66,2.70,2.91,3.51,5.10,5.05,4.78,4.87,4.79,4.64,4.52,4.48,4.38,4.37,4.41,4.59,4.73,4.79,4.76,4.61,4.23,3.77,2.77
+D23,4,2.35,2.50,2.54,2.74,3.39,5.06,5.04,4.90,4.85,4.75,4.62,4.46,4.42,4.41,4.43,4.47,4.66,4.82,4.92,4.91,4.80,4.39,3.79,2.78
+D23,5,2.25,2.47,2.35,2.58,3.46,5.13,5.24,5.07,4.94,4.67,4.50,4.34,4.26,4.20,4.20,4.32,4.55,4.78,4.99,5.13,5.09,4.65,3.93,2.89
+D23,6,2.21,2.15,2.08,2.31,3.43,5.26,6.05,5.50,5.21,4.87,4.63,4.39,4.29,4.16,4.01,4.04,4.15,4.36,4.70,5.05,5.18,4.96,4.09,2.92
+D23,7,2.22,2.20,2.07,2.23,3.70,5.48,6.02,5.70,5.17,5.07,4.74,4.52,4.49,4.09,4.04,3.85,3.91,4.10,4.44,4.74,5.00,4.94,4.19,3.07
+D23,8,2.46,2.55,2.47,2.37,3.80,5.19,4.40,5.55,4.96,5.04,4.78,4.64,4.66,4.28,4.39,3.98,3.92,4.20,4.42,4.55,4.96,4.81,4.37,3.26
+D23,9,2.46,2.55,2.47,2.37,3.80,5.19,4.40,5.55,4.96,5.04,4.78,4.64,4.66,4.28,4.39,3.98,3.92,4.20,4.42,4.55,4.96,4.81,4.37,3.26
\ No newline at end of file