def run_both_with_existing_schedule(oemoef_schedule_file_name,
hal_schedule_sim_name, time_range, pv_path,
hal_result_file_name,
oemof_result_file_name):
# generate data
paths = create_test_data(time_range, False, pv_data_path=pv_path)
# load data
load_data = pd.read_csv(paths['load_data'])['power[W]']
pv_data = pd.read_csv(paths['pv_data'])['power[W]']
heat_load_data = pd.read_csv(paths['load_heat_data'])['power[W]']
date_time_index = pd.date_range(time_range[0],
periods=len(pv_data),
freq='min')
hal_scheduled_es = build_es_from_hal_sim_results(
date_time_index, pv_data, load_data, heat_load_data,
Path.cwd() / 'hal' / 'sim_results' / hal_schedule_sim_name,
hal_schedule_sim_name)
# run oemof with existing schedule
oemof_scheduled_es = build_es_from_oemof_sim_results(
date_time_index, pv_data, load_data, heat_load_data,
Path.cwd() / 'oemof_runs' / 'results', oemoef_schedule_file_name)
run_oemof(oemof_scheduled_es, oemof_result_file_name)
run_oemof(hal_scheduled_es, hal_result_file_name)
def run_simulation(time_range, pv_path, heat_load_path, hal_result_sim_name,
oemof_result_file_name):
# generate data
paths = create_test_data(time_range,
False,
pv_data_path=pv_path,
heat_load_path=heat_load_path)
# load data
load_data = pd.read_csv(paths['load_data'])['power[W]']
pv_data = pd.read_csv(paths['pv_data'])['power[W]']
heat_load_data = pd.read_csv(paths['load_heat_data'])['power[W]']
date_time_index = pd.date_range(time_range[0],
periods=len(pv_data),
freq='min')
es = build_es(date_time_index, pv_data, load_data, heat_load_data)
run_hal(es, hal_result_sim_name)
run_oemof(es, oemof_result_file_name)
def run_altered_pv(time_range,
pv_data_path: str,
oemof_control_file_name,
tau=30,
std=1000,
i=0):
paths = create_test_data(time_range, plot=False, pv_data_path=pv_data_path)
pv_data = pd.read_csv(paths['pv_data'], parse_dates=True, index_col=0)
noise = red_noise(pv_data, tau, std)
# plt.plot(noise)
# plt.show()
pv_data['power[W]'] = pv_data['power[W]'] + noise
modified_pv = pv_data.clip_lower(0)
modified_pv_path = '/'.join(
pv_data_path.split('/')[:-1]) + '/noised_pv_data.csv'
modified_pv.to_csv(modified_pv_path)
oemof_file_name = f"ensemble_{tau}_{std}_{i}.oemof"
hal_sim_name = f"ensemble_{tau}_{std}_{i}"
run_with_existing_schedule(oemof_control_file_name, time_range,
modified_pv_path, hal_sim_name, oemof_file_name)
print(Path.cwd())
print(os.getcwd())
# super important to be able to run hal outside of its own context, otherwise "module not found"-errors will occur
sys.path.append(str(Path.cwd() / 'hal'))
sys.path.append(str(Path.cwd() / 'hal' / 'src'))
import oemof.solph as solph
import pandas as pd
# generate data
day = 60 * 24
week = day * 7
month = week * 4
time_window = day
time_range = ['2016-09-01', '2016-10-01']
paths = create_test_data(time_range, False)
# load data
load_data = pd.read_csv(paths['load_data'])['power[W]']
pv_data = pd.read_csv(paths['pv_data'])['power[W]']
heat_load_data = pd.read_csv(paths['load_heat_data'])['power[W]']
date_time_index = pd.date_range(time_range[0],
periods=len(pv_data),
freq='min')
no_of_datapoints = len(load_data)
no_of_chunks = math.ceil(no_of_datapoints / time_window)
split_load_data = np.array_split(load_data, no_of_chunks)
split_pv_data = np.array_split(pv_data, no_of_chunks)
split_heat_load_data = np.array_split(heat_load_data, no_of_chunks)