Removed t_history

Model_Library @ c3fb8e02

-Subproject commit ebf6f6bbbd721cd1a91c1f51bd460896aee297a9
+Subproject commit c3fb8e02ab0938692ad3695e4deedb3d59aa6ad4

runme_community.py

@@ -12,11 +12,11 @@ from Model_Library.Prosumer.scripts.results_evaluation.results_evaluation import
 
 import Model_Library.District.main_district as main_district
 
-def process_each_prosumer(prosumer_name, prosumer_specification, commentary, t_start, t_horizon, t_step, t_history, prosumer_strategy):
+def process_each_prosumer(prosumer_name, prosumer_specification, commentary, t_start, t_horizon, t_step, prosumer_strategy):
     try:
         before_setup = time.time()
         # Start main programme
-        prosumer = main.Main(prosumer_name, prosumer_specification, t_start, t_horizon, t_step, t_history, commentary)
+        prosumer = main.Main(prosumer_name, prosumer_specification, t_start, t_horizon, t_step, commentary)
         after_setup = time.time()
         print("process_each_prosumer:\tProsumer Construction [s]: \t" + str(after_setup - before_setup))
 
@@ -40,7 +40,6 @@ if __name__ == "__main__":
     t_start = pd.Timestamp("2019-05-10 00:00:00") # start time of simulation
     t_horizon = 240 # number of time steps to be simulated
     t_step = 1 # length of a time step in hours
-    t_history = 0 # number of hours before actual simulation interval for the demand generator to be able to make required predictions
 
     # Path to local data - this is only used when selecting local mode
     # 'data_path': path to file specifying where input profiles are located
@@ -73,12 +72,12 @@ if __name__ == "__main__":
     if parallel_processing:
         count_processes = len(prosumer_dict.keys())
         pool = Pool(os.cpu_count())
-        parallel_func = partial(process_each_prosumer, commentary = commentary, t_start = t_start, t_horizon = t_horizon, t_step = t_step, t_history = t_history, prosumer_strategy = ps_strategy)
+        parallel_func = partial(process_each_prosumer, commentary = commentary, t_start = t_start, t_horizon = t_horizon, t_step = t_step, prosumer_strategy = ps_strategy)
         mapped_values = list(tqdm(pool.map(parallel_func, list(prosumer_dict.keys()), list(prosumer_dict.values())), total = count_processes))
     # Normal processing, one core only
     else:
         for prosumer_name in list(prosumer_dict.keys()):
-            final_prosumer_dict[prosumer_name] = process_each_prosumer(prosumer_name, prosumer_dict[prosumer_name], commentary, t_start, t_horizon, t_step, t_history, ps_strategy)
+            final_prosumer_dict[prosumer_name] = process_each_prosumer(prosumer_name, prosumer_dict[prosumer_name], commentary, t_start, t_horizon, t_step, ps_strategy)
     after_optimization = time.time()
     print("runme:\t\t\tProsumer Optimization [s]: \t" + str(after_optimization - before_optimization))
 
@@ -110,7 +109,7 @@ print("runme:\t\t\tCommunity Assets Setup [s]: \t" + str(before_community_assets
 
 # initialize community component in the same way prosumers are.
 # The difference is that they are not directly optimized
-comm_assets = main.Main_CA(ca_dict, t_start, t_horizon, t_step, t_history, commentary)
+comm_assets = main.Main_CA(ca_dict, t_start, t_horizon, t_step, commentary)
 after_community_assets = time.time()
 print("runme:\t\t\tCommunity Assets Constr. [s]: \t" + str(after_community_assets - before_community_assets))