def building_loader(building_attributes, solar_profile, building_ids,
buildings_states_actions):
with open(building_attributes) as json_file:
data = json.load(json_file)
buildings, observation_spaces, action_spaces = [], [], []
for uid, attributes in zip(data, data.values()):
if uid in building_ids:
heat_pump = HeatPump(
nominal_power=attributes['Heat_Pump']['nominal_power'],
eta_tech=attributes['Heat_Pump']['technical_efficiency'],
t_target_heating=attributes['Heat_Pump']['t_target_heating'],
t_target_cooling=attributes['Heat_Pump']['t_target_cooling'])
electric_heater = ElectricHeater(
nominal_power=attributes['Electric_Water_Heater']
['nominal_power'],
efficiency=attributes['Electric_Water_Heater']['efficiency'])
chilled_water_tank = EnergyStorage(
capacity=attributes['Chilled_Water_Tank']['capacity'],
loss_coeff=attributes['Chilled_Water_Tank']
['loss_coefficient'])
dhw_tank = EnergyStorage(
capacity=attributes['DHW_Tank']['capacity'],
loss_coeff=attributes['DHW_Tank']['loss_coefficient'])
building = Building(buildingId=uid,
dhw_storage=dhw_tank,
cooling_storage=chilled_water_tank,
dhw_heating_device=electric_heater,
cooling_device=heat_pump)
with open('data//' + uid + '.csv') as csv_file:
data = pd.read_csv(csv_file)
building.sim_results['cooling_demand'] = data['Cooling Load [kWh]']
building.sim_results['dhw_demand'] = data['DHW Heating [kWh]']
building.sim_results['non_shiftable_load'] = data[
'Equipment Electric Power [kWh]']
building.sim_results['day'] = data['Day Type']
building.sim_results['hour'] = data['Hour']
building.sim_results['daylight_savings_status'] = data[
'Daylight Savings Status']
building.sim_results['t_out'] = data[
'Outdoor Drybulb Temperature [C]']
building.sim_results['rh_out'] = data[
'Outdoor Relative Humidity [%]']
building.sim_results['diffuse_solar_rad'] = data[
'Diffuse Solar Radiation [W/m2]']
building.sim_results['direct_solar_rad'] = data[
'Direct Solar Radiation [W/m2]']
building.sim_results['t_in'] = data['Indoor Temperature [C]']
building.sim_results['avg_unmet_setpoint'] = data[
'Average Unmet Cooling Setpoint Difference [C]']
building.sim_results['rh_in'] = data[
'Indoor Relative Humidity [%]']
with open(solar_profile) as csv_file:
data = pd.read_csv(csv_file)
building.sim_results[
'solar_gen'] = attributes['Solar_Power_Installed(kW)'] * data[
'Hourly Data: AC inverter power (W)'] / 1000
# Finding the max and min possible values of all the states, which can then be used by the RL agent to scale the states and train any function approximators more effectively
s_low, s_high = [], []
for state_name, value in zip(
buildings_states_actions[uid]['states'],
buildings_states_actions[uid]['states'].values()):
if value == True:
if state_name != 'cooling_storage_soc' and state_name != 'dhw_storage_soc':
s_low.append(building.sim_results[state_name].min())
s_high.append(building.sim_results[state_name].max())
else:
s_low.append(0.0)
s_high.append(1.0)
a_low, a_high = [], []
for state_name, value in zip(
buildings_states_actions[uid]['actions'],
buildings_states_actions[uid]['actions'].values()):
if value == True:
a_low.append(0.0)
a_high.append(1.0)
building.set_state_space(np.array(s_high), np.array(s_low))
building.set_action_space(np.array(a_high), np.array(a_low))
observation_spaces.append(building.observation_space)
action_spaces.append(building.action_space)
buildings.append(building)
auto_size(buildings)
return buildings, observation_spaces, action_spaces
def building_loader(data_path, building_attributes, weather_file, solar_profile, building_ids, buildings_states_actions, save_memory = True):
with open(building_attributes) as json_file:
data = json.load(json_file)
buildings, observation_spaces, action_spaces = {},[],[]
s_low_central_agent, s_high_central_agent, appended_states = [], [], []
a_low_central_agent, a_high_central_agent, appended_actions = [], [], []
for uid, attributes in zip(data, data.values()):
if uid in building_ids:
heat_pump = HeatPump(nominal_power = attributes['Heat_Pump']['nominal_power'],
eta_tech = attributes['Heat_Pump']['technical_efficiency'],
t_target_heating = attributes['Heat_Pump']['t_target_heating'],
t_target_cooling = attributes['Heat_Pump']['t_target_cooling'], save_memory = save_memory)
electric_heater = ElectricHeater(nominal_power = attributes['Electric_Water_Heater']['nominal_power'],
efficiency = attributes['Electric_Water_Heater']['efficiency'], save_memory = save_memory)
chilled_water_tank = EnergyStorage(capacity = attributes['Chilled_Water_Tank']['capacity'],
loss_coeff = attributes['Chilled_Water_Tank']['loss_coefficient'], save_memory = save_memory)
dhw_tank = EnergyStorage(capacity = attributes['DHW_Tank']['capacity'],
loss_coeff = attributes['DHW_Tank']['loss_coefficient'], save_memory = save_memory)
building = Building(buildingId = uid, dhw_storage = dhw_tank, cooling_storage = chilled_water_tank, dhw_heating_device = electric_heater, cooling_device = heat_pump, save_memory = save_memory)
data_file = str(uid) + '.csv'
simulation_data = data_path / data_file
with open(simulation_data) as csv_file:
data = pd.read_csv(csv_file)
building.sim_results['cooling_demand'] = list(data['Cooling Load [kWh]'])
building.sim_results['dhw_demand'] = list(data['DHW Heating [kWh]'])
building.sim_results['non_shiftable_load'] = list(data['Equipment Electric Power [kWh]'])
building.sim_results['month'] = list(data['Month'])
building.sim_results['day'] = list(data['Day Type'])
building.sim_results['hour'] = list(data['Hour'])
building.sim_results['daylight_savings_status'] = list(data['Daylight Savings Status'])
building.sim_results['t_in'] = list(data['Indoor Temperature [C]'])
building.sim_results['avg_unmet_setpoint'] = list(data['Average Unmet Cooling Setpoint Difference [C]'])
building.sim_results['rh_in'] = list(data['Indoor Relative Humidity [%]'])
with open(weather_file) as csv_file:
weather_data = pd.read_csv(csv_file)
building.sim_results['t_out'] = list(weather_data['Outdoor Drybulb Temperature [C]'])
building.sim_results['rh_out'] = list(weather_data['Outdoor Relative Humidity [%]'])
building.sim_results['diffuse_solar_rad'] = list(weather_data['Diffuse Solar Radiation [W/m2]'])
building.sim_results['direct_solar_rad'] = list(weather_data['Direct Solar Radiation [W/m2]'])
# Reading weather forecasts
building.sim_results['t_out_pred_6h'] = list(weather_data['6h Prediction Outdoor Drybulb Temperature [C]'])
building.sim_results['t_out_pred_12h'] = list(weather_data['12h Prediction Outdoor Drybulb Temperature [C]'])
building.sim_results['t_out_pred_24h'] = list(weather_data['24h Prediction Outdoor Drybulb Temperature [C]'])
building.sim_results['rh_out_pred_6h'] = list(weather_data['6h Prediction Outdoor Relative Humidity [%]'])
building.sim_results['rh_out_pred_12h'] = list(weather_data['12h Prediction Outdoor Relative Humidity [%]'])
building.sim_results['rh_out_pred_24h'] = list(weather_data['24h Prediction Outdoor Relative Humidity [%]'])
building.sim_results['diffuse_solar_rad_pred_6h'] = list(weather_data['6h Prediction Diffuse Solar Radiation [W/m2]'])
building.sim_results['diffuse_solar_rad_pred_12h'] = list(weather_data['12h Prediction Diffuse Solar Radiation [W/m2]'])
building.sim_results['diffuse_solar_rad_pred_24h'] = list(weather_data['24h Prediction Diffuse Solar Radiation [W/m2]'])
building.sim_results['direct_solar_rad_pred_6h'] = list(weather_data['6h Prediction Direct Solar Radiation [W/m2]'])
building.sim_results['direct_solar_rad_pred_12h'] = list(weather_data['12h Prediction Direct Solar Radiation [W/m2]'])
building.sim_results['direct_solar_rad_pred_24h'] = list(weather_data['24h Prediction Direct Solar Radiation [W/m2]'])
# Reading the building attributes
building.building_type = attributes['Building_Type']
building.climate_zone = attributes['Climate_Zone']
building.solar_power_capacity = attributes['Solar_Power_Installed(kW)']
with open(solar_profile) as csv_file:
data = pd.read_csv(csv_file)
building.sim_results['solar_gen'] = list(attributes['Solar_Power_Installed(kW)']*data['Hourly Data: AC inverter power (W)']/1000)
# Finding the max and min possible values of all the states, which can then be used by the RL agent to scale the states and train any function approximators more effectively
s_low, s_high = [], []
for state_name, value in zip(buildings_states_actions[uid]['states'], buildings_states_actions[uid]['states'].values()):
if value == True:
if state_name == "net_electricity_consumption":
# lower and upper bounds of net electricity consumption are rough estimates and may not be completely accurate. Scaling this state-variable using these bounds may result in normalized values above 1 or below 0.
_net_elec_cons_upper_bound = max(np.array(building.sim_results['non_shiftable_load']) - np.array(building.sim_results['solar_gen']) + np.array(building.sim_results['dhw_demand'])/.8 + np.array(building.sim_results['cooling_demand']) + building.dhw_storage.capacity/.8 + building.cooling_storage.capacity/2)
s_low.append(0.)
s_high.append(_net_elec_cons_upper_bound)
s_low_central_agent.append(0.)
s_high_central_agent.append(_net_elec_cons_upper_bound)
elif state_name != 'cooling_storage_soc' and state_name != 'dhw_storage_soc':
s_low.append(min(building.sim_results[state_name]))
s_high.append(max(building.sim_results[state_name]))
# Create boundaries of the observation space of a centralized agent (if a central agent is being used instead of decentralized ones). We include all the weather variables used as states, and use the list appended_states to make sure we don't include any repeated states (i.e. weather variables measured by different buildings)
if state_name in ['t_in', 'avg_unmet_setpoint', 'rh_in', 'non_shiftable_load', 'solar_gen']:
s_low_central_agent.append(min(building.sim_results[state_name]))
s_high_central_agent.append(max(building.sim_results[state_name]))
elif state_name not in appended_states:
s_low_central_agent.append(min(building.sim_results[state_name]))
s_high_central_agent.append(max(building.sim_results[state_name]))
appended_states.append(state_name)
else:
s_low.append(0.0)
s_high.append(1.0)
s_low_central_agent.append(0.0)
s_high_central_agent.append(1.0)
'''The energy storage (tank) capacity indicates how many times bigger the tank is compared to the maximum hourly energy demand of the building (cooling or DHW respectively), which sets a lower bound for the action of 1/tank_capacity, as the energy storage device can't provide the building with more energy than it will ever need for a given hour. The heat pump is sized using approximately the maximum hourly energy demand of the building (after accounting for the COP, see function autosize). Therefore, we make the fair assumption that the action also has an upper bound equal to 1/tank_capacity. This boundaries should speed up the learning process of the agents and make them more stable rather than if we just set them to -1 and 1. I.e. if Chilled_Water_Tank.Capacity is 3 (3 times the max. hourly demand of the building in the entire year), its actions will be bounded between -1/3 and 1/3'''
a_low, a_high = [], []
for action_name, value in zip(buildings_states_actions[uid]['actions'], buildings_states_actions[uid]['actions'].values()):
if value == True:
if action_name =='cooling_storage':
# Avoid division by 0
if attributes['Chilled_Water_Tank']['capacity'] > 0.000001:
a_low.append(max(-1.0/attributes['Chilled_Water_Tank']['capacity'], -1.0))
a_high.append(min(1.0/attributes['Chilled_Water_Tank']['capacity'], 1.0))
a_low_central_agent.append(max(-1.0/attributes['Chilled_Water_Tank']['capacity'], -1.0))
a_high_central_agent.append(min(1.0/attributes['Chilled_Water_Tank']['capacity'], 1.0))
else:
a_low.append(-1.0)
a_high.append(1.0)
a_low_central_agent.append(-1.0)
a_high_central_agent.append(1.0)
else:
if attributes['DHW_Tank']['capacity'] > 0.000001:
a_low.append(max(-1.0/attributes['DHW_Tank']['capacity'], -1.0))
a_high.append(min(1.0/attributes['DHW_Tank']['capacity'], 1.0))
a_low_central_agent.append(max(-1.0/attributes['DHW_Tank']['capacity'], -1.0))
a_high_central_agent.append(min(1.0/attributes['DHW_Tank']['capacity'], 1.0))
else:
a_low.append(-1.0)
a_high.append(1.0)
a_low_central_agent.append(-1.0)
a_high_central_agent.append(1.0)
building.set_state_space(np.array(s_high), np.array(s_low))
building.set_action_space(np.array(a_high), np.array(a_low))
observation_spaces.append(building.observation_space)
action_spaces.append(building.action_space)
buildings[uid] = building
observation_space_central_agent = spaces.Box(low=np.float32(np.array(s_low_central_agent)), high=np.float32(np.array(s_high_central_agent)), dtype=np.float32)
action_space_central_agent = spaces.Box(low=np.float32(np.array(a_low_central_agent)), high=np.float32(np.array(a_high_central_agent)), dtype=np.float32)
for building in buildings.values():
# If the DHW device is a HeatPump
if isinstance(building.dhw_heating_device, HeatPump):
# Calculating COPs of the heat pumps for every hour
building.dhw_heating_device.cop_heating = building.dhw_heating_device.eta_tech*(building.dhw_heating_device.t_target_heating + 273.15)/(building.dhw_heating_device.t_target_heating - weather_data['Outdoor Drybulb Temperature [C]'])
building.dhw_heating_device.cop_heating[building.dhw_heating_device.cop_heating < 0] = 20.0
building.dhw_heating_device.cop_heating[building.dhw_heating_device.cop_heating > 20] = 20.0
building.dhw_heating_device.cop_heating = building.dhw_heating_device.cop_heating.to_numpy()
building.cooling_device.cop_cooling = building.cooling_device.eta_tech*(building.cooling_device.t_target_cooling + 273.15)/(weather_data['Outdoor Drybulb Temperature [C]'] - building.cooling_device.t_target_cooling)
building.cooling_device.cop_cooling[building.cooling_device.cop_cooling < 0] = 20.0
building.cooling_device.cop_cooling[building.cooling_device.cop_cooling > 20] = 20.0
building.cooling_device.cop_cooling = building.cooling_device.cop_cooling.to_numpy()
building.reset()
auto_size(buildings)
return buildings, observation_spaces, action_spaces, observation_space_central_agent, action_space_central_agent
def building_loader(data_path, building_attributes, weather_file, solar_profile, building_ids, buildings_states_actions):
with open(building_attributes) as json_file:
data = json.load(json_file)
buildings, observation_spaces, action_spaces = {},[],[]
for uid, attributes in zip(data, data.values()):
if uid in building_ids:
heat_pump = HeatPump(nominal_power = attributes['Heat_Pump']['nominal_power'],
eta_tech = attributes['Heat_Pump']['technical_efficiency'],
t_target_heating = attributes['Heat_Pump']['t_target_heating'],
t_target_cooling = attributes['Heat_Pump']['t_target_cooling'])
electric_heater = ElectricHeater(nominal_power = attributes['Electric_Water_Heater']['nominal_power'],
efficiency = attributes['Electric_Water_Heater']['efficiency'])
chilled_water_tank = EnergyStorage(capacity = attributes['Chilled_Water_Tank']['capacity'],
loss_coeff = attributes['Chilled_Water_Tank']['loss_coefficient'])
dhw_tank = EnergyStorage(capacity = attributes['DHW_Tank']['capacity'],
loss_coeff = attributes['DHW_Tank']['loss_coefficient'])
building = Building(buildingId = uid, dhw_storage = dhw_tank, cooling_storage = chilled_water_tank, dhw_heating_device = electric_heater, cooling_device = heat_pump)
data_file = str(uid) + '.csv'
simulation_data = data_path / data_file
with open(simulation_data) as csv_file:
data = pd.read_csv(csv_file)
building.sim_results['cooling_demand'] = list(data['Cooling Load [kWh]'])
building.sim_results['dhw_demand'] = list(data['DHW Heating [kWh]'])
building.sim_results['non_shiftable_load'] = list(data['Equipment Electric Power [kWh]'])
building.sim_results['month'] = list(data['Month'])
building.sim_results['day'] = list(data['Day Type'])
building.sim_results['hour'] = list(data['Hour'])
building.sim_results['daylight_savings_status'] = list(data['Daylight Savings Status'])
building.sim_results['t_in'] = list(data['Indoor Temperature [C]'])
building.sim_results['avg_unmet_setpoint'] = list(data['Average Unmet Cooling Setpoint Difference [C]'])
building.sim_results['rh_in'] = list(data['Indoor Relative Humidity [%]'])
with open(weather_file) as csv_file:
weather_data = pd.read_csv(csv_file)
building.sim_results['t_out'] = list(weather_data['Outdoor Drybulb Temperature [C]'])
building.sim_results['rh_out'] = list(weather_data['Outdoor Relative Humidity [%]'])
building.sim_results['diffuse_solar_rad'] = list(weather_data['Diffuse Solar Radiation [W/m2]'])
building.sim_results['direct_solar_rad'] = list(weather_data['Direct Solar Radiation [W/m2]'])
# Reading weather forecasts
building.sim_results['t_out_pred_6h'] = list(weather_data['6h Prediction Outdoor Drybulb Temperature [C]'])
building.sim_results['t_out_pred_12h'] = list(weather_data['12h Prediction Outdoor Drybulb Temperature [C]'])
building.sim_results['t_out_pred_24h'] = list(weather_data['24h Prediction Outdoor Drybulb Temperature [C]'])
building.sim_results['rh_out_pred_6h'] = list(weather_data['6h Prediction Outdoor Relative Humidity [%]'])
building.sim_results['rh_out_pred_12h'] = list(weather_data['12h Prediction Outdoor Relative Humidity [%]'])
building.sim_results['rh_out_pred_24h'] = list(weather_data['24h Prediction Outdoor Relative Humidity [%]'])
building.sim_results['diffuse_solar_rad_pred_6h'] = list(weather_data['6h Prediction Diffuse Solar Radiation [W/m2]'])
building.sim_results['diffuse_solar_rad_pred_12h'] = list(weather_data['12h Prediction Diffuse Solar Radiation [W/m2]'])
building.sim_results['diffuse_solar_rad_pred_24h'] = list(weather_data['24h Prediction Diffuse Solar Radiation [W/m2]'])
building.sim_results['direct_solar_rad_pred_6h'] = list(weather_data['6h Prediction Direct Solar Radiation [W/m2]'])
building.sim_results['direct_solar_rad_pred_12h'] = list(weather_data['12h Prediction Direct Solar Radiation [W/m2]'])
building.sim_results['direct_solar_rad_pred_24h'] = list(weather_data['24h Prediction Direct Solar Radiation [W/m2]'])
# Reading the building attributes
building.building_type = attributes['Building_Type']
building.climate_zone = attributes['Climate_Zone']
building.solar_power_capacity = attributes['Solar_Power_Installed(kW)']
with open(solar_profile) as csv_file:
data = pd.read_csv(csv_file)
building.sim_results['solar_gen'] = list(attributes['Solar_Power_Installed(kW)']*data['Hourly Data: AC inverter power (W)']/1000)
# Finding the max and min possible values of all the states, which can then be used by the RL agent to scale the states and train any function approximators more effectively
s_low, s_high = [], []
for state_name, value in zip(buildings_states_actions[uid]['states'], buildings_states_actions[uid]['states'].values()):
if value == True:
if state_name != 'cooling_storage_soc' and state_name != 'dhw_storage_soc':
s_low.append(min(building.sim_results[state_name]))
s_high.append(max(building.sim_results[state_name]))
else:
s_low.append(0.0)
s_high.append(1.0)
a_low, a_high = [], []
for state_name, value in zip(buildings_states_actions[uid]['actions'], buildings_states_actions[uid]['actions'].values()):
if value == True:
a_low.append(0.0)
a_high.append(1.0)
building.set_state_space(np.array(s_high), np.array(s_low))
building.set_action_space(np.array(a_high), np.array(a_low))
observation_spaces.append(building.observation_space)
action_spaces.append(building.action_space)
buildings[uid] = building
for building in buildings.values():
# If the DHW device is a HeatPump
if isinstance(building.dhw_heating_device, HeatPump):
# Calculating COPs of the heat pumps for every hour
building.dhw_heating_device.cop_heating = building.dhw_heating_device.eta_tech*(building.dhw_heating_device.t_target_heating + 273.15)/(building.dhw_heating_device.t_target_heating - weather_data['Outdoor Drybulb Temperature [C]'])
building.dhw_heating_device.cop_heating[building.dhw_heating_device.cop_heating < 0] = 20.0
building.dhw_heating_device.cop_heating[building.dhw_heating_device.cop_heating > 20] = 20.0
building.dhw_heating_device.cop_heating = building.dhw_heating_device.cop_heating.to_numpy()
building.cooling_device.cop_cooling = building.cooling_device.eta_tech*(building.cooling_device.t_target_cooling + 273.15)/(weather_data['Outdoor Drybulb Temperature [C]'] - building.cooling_device.t_target_cooling)
building.cooling_device.cop_cooling[building.cooling_device.cop_cooling < 0] = 20.0
building.cooling_device.cop_cooling[building.cooling_device.cop_cooling > 20] = 20.0
building.cooling_device.cop_cooling = building.cooling_device.cop_cooling.to_numpy()
auto_size(buildings)
return buildings, observation_spaces, action_spaces