import logging
from typing import Any, List, Mapping, Tuple, Union
from gymnasium import spaces
import numpy as np
import pandas as pd
import torch
from citylearn.base import Environment, EpisodeTracker
from citylearn.data import CarbonIntensity, EnergySimulation, Pricing, TOLERANCE, Weather, ZERO_DIVISION_PLACEHOLDER
from citylearn.dynamics import Dynamics, LSTMDynamics
from citylearn.electric_vehicle_charger import Charger
from citylearn.energy_model import Battery, ElectricDevice, ElectricHeater, HeatPump, PV, StorageTank
from citylearn.occupant import LogisticRegressionOccupant, Occupant
from citylearn.power_outage import PowerOutage
from citylearn.preprocessing import Normalize, PeriodicNormalization
LOGGER = logging.getLogger()
logging.basicConfig(level=logging.INFO)
[docs]
class Building(Environment):
r"""Base class for building.
Parameters
----------
energy_simulation : EnergySimulation
Temporal features, cooling, heating, dhw and plug loads, solar generation and indoor environment time series.
weather : Weather
Outdoor weather conditions and forecasts time sereis.
observation_metadata : dict
Mapping of active and inactive observations.
action_metadata : dict
Mapping od active and inactive actions.
episode_tracker: EpisodeTracker, optional
:py:class:`citylearn.base.EpisodeTracker` object used to keep track of current episode time steps
for reading observations from data files.
carbon_intensity : CarbonIntensity, optional
Carbon dioxide emission rate time series.
pricing : Pricing, optional
Energy pricing and forecasts time series.
dhw_storage : StorageTank, optional
Hot water storage object for domestic hot water.
cooling_storage : StorageTank, optional
Cold water storage object for space cooling.
heating_storage : StorageTank, optional
Hot water storage object for space heating.
electrical_storage : Battery, optional
Electric storage object for meeting electric loads.
dhw_device : Union[HeatPump, ElectricHeater], optional
Electric device for meeting hot domestic hot water demand and charging `dhw_storage`.
cooling_device : HeatPump, optional
Electric device for meeting space cooling demand and charging `cooling_storage`.
heating_device : Union[HeatPump, ElectricHeater], optional
Electric device for meeting space heating demand and charging `heating_storage`.
pv : PV, optional
PV object for offsetting electricity demand from grid.
name : str, optional
Unique building name.
observation_space_limit_delta: float, default: 0.0
+/- buffer for observation space limits after they have been dynamically calculated.
maximum_temperature_delta: float, default: 20.0
Expected maximum absolute temperature delta above and below indoor dry-bulb temperature in [C].
demand_observation_limit_factor: float, default: 1.15
Multiplier for maximum cooling/heating/dhw demand observations when setting observation limits.
simulate_power_outage: bool, default: False
Whether to allow time steps when the grid is unavailable and loads must be met using only the
building's downward flexibility resources.
stochastic_power_outage: bool, default: False
Whether to use a stochastic function to determine outage time steps otherwise,
:py:class:`citylearn.building.Building.energy_simulation.power_outage` time series is used.
stochastic_power_outage_model: PowerOutage, optional
Power outage model class used to generate stochastic power outage signals.
carbon_intensity : CarbonIntensity, optional
Carbon dioxide emission rate time series.
electric_vehicle_chargers : Charger, optional
Electric Vehicle Chargers associated with the building.
Other Parameters
----------------
**kwargs : Any
Other keyword arguments used to initialize super class.
"""
def __init__(
self, energy_simulation: EnergySimulation, weather: Weather, observation_metadata: Mapping[str, bool],
action_metadata: Mapping[str, bool], episode_tracker: EpisodeTracker,
carbon_intensity: CarbonIntensity = None,
pricing: Pricing = None, dhw_storage: StorageTank = None, cooling_storage: StorageTank = None,
heating_storage: StorageTank = None, electrical_storage: Battery = None,
dhw_device: Union[HeatPump, ElectricHeater] = None, cooling_device: HeatPump = None,
heating_device: Union[HeatPump, ElectricHeater] = None, pv: PV = None, name: str = None,
maximum_temperature_delta: float = None, observation_space_limit_delta: float = None,
demand_observation_limit_factor: float = None, simulate_power_outage: bool = None,
stochastic_power_outage: bool = None, stochastic_power_outage_model: PowerOutage = None,
electric_vehicle_chargers: List[Charger] = None, **kwargs: Any
):
self.name = name
self.dhw_storage = dhw_storage
self.cooling_storage = cooling_storage
self.heating_storage = heating_storage
self.electrical_storage = electrical_storage
self.dhw_device = dhw_device
self.cooling_device = cooling_device
self.heating_device = heating_device
self.__non_shiftable_load_device = ElectricDevice(0.0)
self.pv = pv
super().__init__(
seconds_per_time_step=kwargs.get('seconds_per_time_step'),
random_seed=kwargs.get('random_seed'),
episode_tracker=episode_tracker
)
self.stochastic_power_outage_model = stochastic_power_outage_model
self.electric_vehicle_chargers = electric_vehicle_chargers
self.energy_simulation = energy_simulation
self.weather = weather
self.carbon_intensity = carbon_intensity
self.pricing = pricing
self.observation_metadata = observation_metadata
self.action_metadata = action_metadata
self.observation_space_limit_delta = observation_space_limit_delta
self.maximum_temperature_delta = maximum_temperature_delta
self.demand_observation_limit_factor = demand_observation_limit_factor
self.simulate_power_outage = simulate_power_outage
self.stochastic_power_outage = stochastic_power_outage
self.non_periodic_normalized_observation_space_limits = None
self.periodic_normalized_observation_space_limits = None
self.observation_space = self.estimate_observation_space(include_all=False, normalize=False)
self.action_space = self.estimate_action_space()
@property
def energy_simulation(self) -> EnergySimulation:
"""Temporal features, cooling, heating, dhw and plug loads, solar generation and indoor environment time series."""
return self.__energy_simulation
@property
def weather(self) -> Weather:
"""Outdoor weather conditions and forecasts time series."""
return self.__weather
@property
def observation_metadata(self) -> Mapping[str, bool]:
"""Mapping of active and inactive observations."""
return self.__observation_metadata
@property
def action_metadata(self) -> Mapping[str, bool]:
"""Mapping od active and inactive actions."""
return self.__action_metadata
@property
def carbon_intensity(self) -> CarbonIntensity:
"""Carbon dioxide emission rate time series."""
return self.__carbon_intensity
@property
def pricing(self) -> Pricing:
"""Energy pricing and forecasts time series."""
return self.__pricing
@property
def dhw_storage(self) -> StorageTank:
"""Hot water storage object for domestic hot water."""
return self.__dhw_storage
@property
def cooling_storage(self) -> StorageTank:
"""Cold water storage object for space cooling."""
return self.__cooling_storage
@property
def heating_storage(self) -> StorageTank:
"""Hot water storage object for space heating."""
return self.__heating_storage
@property
def electrical_storage(self) -> Battery:
"""Electric storage object for meeting electric loads."""
return self.__electrical_storage
@property
def dhw_device(self) -> Union[HeatPump, ElectricHeater]:
"""Electric device for meeting hot domestic hot water demand and charging `dhw_storage`."""
return self.__dhw_device
@property
def cooling_device(self) -> HeatPump:
"""Electric device for meeting space cooling demand and charging `cooling_storage`."""
return self.__cooling_device
@property
def heating_device(self) -> Union[HeatPump, ElectricHeater]:
"""Electric device for meeting space heating demand and charging `heating_storage`."""
return self.__heating_device
@property
def non_shiftable_load_device(self) -> ElectricDevice:
"""Generic electric device for meeting non_shiftable_load."""
return self.__non_shiftable_load_device
@property
def pv(self) -> PV:
"""PV object for offsetting electricity demand from grid."""
return self.__pv
@property
def electric_vehicle_chargers(self) -> List[Charger]:
"""Electric Vehicle Chargers associated with the building for charging connected eletric vehicles."""
return self.__electric_vehicle_chargers
@property
def name(self) -> str:
"""Unique building name."""
return self.__name
@property
def observation_space_limit_delta(self) -> float:
"""+/- buffer for observation space limits after they have been dynamically calculated."""
return self.__observation_space_limit_delta
@property
def maximum_temperature_delta(self) -> float:
"""Expected maximum absolute temperature delta above and below indoor dry-bulb temperature in [C]."""
return self.__maximum_temperature_delta
@property
def demand_observation_limit_factor(self) -> float:
"""Multiplier for maximum cooling/heating/dhw demand observations when setting observation limits."""
return self.__demand_observation_limit_factor
@property
def simulate_power_outage(self) -> bool:
"""Whether to allow time steps when the grid is unavailable and loads must be met using only the
building's downward flexibility resources."""
return self.__simulate_power_outage
@property
def stochastic_power_outage(self) -> bool:
"""Whether to use a stochastic function to determine outage time steps otherwise,
:py:class:`citylearn.building.Building.energy_simulation.power_outage` time series is used."""
return self.__stochastic_power_outage
@property
def observation_space(self) -> spaces.Box:
"""Agent observation space."""
return self.__observation_space
@property
def action_space(self) -> spaces.Box:
"""Agent action spaces."""
return self.__action_space
@property
def active_observations(self) -> List[str]:
"""Observations in `observation_metadata` with True value i.e. obeservable."""
return [k for k, v in self.observation_metadata.items() if v]
@property
def active_actions(self) -> List[str]:
"""Actions in `action_metadata` with True value i.e.
indicates which storage systems are to be controlled during simulation."""
return [k for k, v in self.action_metadata.items() if v]
@property
def net_electricity_consumption_emission_without_storage_and_pv(self) -> np.ndarray:
"""Carbon dioxide emmission from `net_electricity_consumption_without_storage_pv` time series, in [kg_co2]."""
return (
self.carbon_intensity.carbon_intensity[
0:self.time_step + 1] * self.net_electricity_consumption_without_storage_and_pv
).clip(min=0)
@property
def net_electricity_consumption_cost_without_storage_and_pv(self) -> np.ndarray:
"""net_electricity_consumption_without_storage_and_pv` cost time series, in [$]."""
return self.pricing.electricity_pricing[
0:self.time_step + 1] * self.net_electricity_consumption_without_storage_and_pv
@property
def net_electricity_consumption_without_storage_and_pv(self) -> np.ndarray:
"""Net electricity consumption in the absence of flexibility provided by storage devices,
and self generation time series, in [kWh].
Notes
-----
net_electricity_consumption_without_storage_and_pv =
`net_electricity_consumption_without_storage` - `solar_generation`
"""
return self.net_electricity_consumption_without_storage - self.solar_generation
@property
def net_electricity_consumption_emission_without_storage(self) -> np.ndarray:
"""Carbon dioxide emmission from `net_electricity_consumption_without_storage` time series, in [kg_co2]."""
return (self.carbon_intensity.carbon_intensity[
0:self.time_step + 1] * self.net_electricity_consumption_without_storage).clip(min=0)
@property
def net_electricity_consumption_cost_without_storage(self) -> np.ndarray:
"""`net_electricity_consumption_without_storage` cost time series, in [$]."""
return self.pricing.electricity_pricing[0:self.time_step + 1] * self.net_electricity_consumption_without_storage
@property
def net_electricity_consumption_without_storage(self) -> np.ndarray:
"""net electricity consumption in the absence of flexibility provided by storage devices time series, in [kWh].
Notes
-----
net_electricity_consumption_without_storage = `net_electricity_consumption` - (`cooling_storage_electricity_consumption`
+ `heating_storage_electricity_consumption` + `dhw_storage_electricity_consumption` + `electrical_storage_electricity_consumption` + `charger_electricity_consumption`)
Regarding electric vehicles there is:
chargers_electricity_consumption -> Sum of the electricity consumption of all electric_vehicle_chargers in the building
So, the first one is subtracted from the net_electricity_consumption, obtaining the energy consumption as if the cars were not used at all.
However, if there are chargers and EVs, they need to charge per usual, so that consumption is added
This is what allows to check if the control mechanism affects the grid balancing scheme for EVs for example.
"""
return self.net_electricity_consumption - np.sum([
self.cooling_storage_electricity_consumption,
self.heating_storage_electricity_consumption,
self.dhw_storage_electricity_consumption,
self.electrical_storage_electricity_consumption,
self.__chargers_electricity_consumption
], axis=0)
@property
def net_electricity_consumption_emission(self) -> np.ndarray:
"""Carbon dioxide emmission from `net_electricity_consumption` time series, in [kg_co2]."""
return self.__net_electricity_consumption_emission[:self.time_step + 1]
@property
def net_electricity_consumption_cost(self) -> np.ndarray:
"""`net_electricity_consumption` cost time series, in [$]."""
return self.__net_electricity_consumption_cost[:self.time_step + 1]
@property
def net_electricity_consumption(self) -> np.ndarray:
"""Net electricity consumption time series, in [kWh]."""
return self.__net_electricity_consumption[:self.time_step + 1]
@property
def cooling_electricity_consumption(self) -> np.ndarray:
"""`cooling_device` net electricity consumption in meeting cooling demand and `cooling_storage` energy demand time series, in [kWh].
"""
return self.cooling_device.electricity_consumption[:self.time_step + 1]
@property
def heating_electricity_consumption(self) -> np.ndarray:
"""`heating_device` net electricity consumption in meeting heating demand and `heating_storage` energy demand time series, in [kWh].
"""
return self.heating_device.electricity_consumption[:self.time_step + 1]
@property
def dhw_electricity_consumption(self) -> np.ndarray:
"""`dhw_device` net electricity consumption in meeting domestic hot water and `dhw_storage` energy demand time series, in [kWh].
"""
return self.dhw_device.electricity_consumption[:self.time_step + 1]
@property
def non_shiftable_load_electricity_consumption(self) -> np.ndarray:
"""`non_shiftable_load_device` net electricity consumption in meeting `non_shiftable_load` energy demand time series, in [kWh].
"""
return self.non_shiftable_load_device.electricity_consumption[:self.time_step + 1]
@property
def cooling_storage_electricity_consumption(self) -> np.ndarray:
"""`cooling_storage` net electricity consumption time series, in [kWh].
Positive values indicate `cooling_device` electricity consumption to charge `cooling_storage` while negative values indicate avoided `cooling_device`
electricity consumption by discharging `cooling_storage` to meet `cooling_demand`.
"""
return self.cooling_device.get_input_power(self.cooling_storage.energy_balance[:self.time_step + 1],
self.weather.outdoor_dry_bulb_temperature[:self.time_step + 1],
False)
@property
def heating_storage_electricity_consumption(self) -> np.ndarray:
"""`heating_storage` net electricity consumption time series, in [kWh].
Positive values indicate `heating_device` electricity consumption to charge `heating_storage` while negative values indicate avoided `heating_device`
electricity consumption by discharging `heating_storage` to meet `heating_demand`.
"""
if isinstance(self.heating_device, HeatPump):
consumption = self.heating_device.get_input_power(self.heating_storage.energy_balance[:self.time_step + 1],
self.weather.outdoor_dry_bulb_temperature[
:self.time_step + 1], True)
else:
consumption = self.heating_device.get_input_power(self.heating_storage.energy_balance[:self.time_step + 1])
return consumption
@property
def dhw_storage_electricity_consumption(self) -> np.ndarray:
"""`dhw_storage` net electricity consumption time series, in [kWh].
Positive values indicate `dhw_device` electricity consumption to charge `dhw_storage` while negative values indicate avoided `dhw_device`
electricity consumption by discharging `dhw_storage` to meet `dhw_demand`.
"""
if isinstance(self.dhw_device, HeatPump):
consumption = self.dhw_device.get_input_power(self.dhw_storage.energy_balance[:self.time_step + 1],
self.weather.outdoor_dry_bulb_temperature[
:self.time_step + 1], True)
else:
consumption = self.dhw_device.get_input_power(self.dhw_storage.energy_balance[:self.time_step + 1])
return consumption
@property
def electrical_storage_electricity_consumption(self) -> np.ndarray:
"""Energy supply from grid and/or `PV` to `electrical_storage` time series, in [kWh]."""
return self.electrical_storage.electricity_consumption[:self.time_step + 1]
@property
def energy_from_cooling_device_to_cooling_storage(self) -> np.ndarray:
"""Energy supply from `cooling_device` to `cooling_storage` time series, in [kWh]."""
return self.cooling_storage.energy_balance.clip(min=0)[:self.time_step + 1]
@property
def energy_from_heating_device_to_heating_storage(self) -> np.ndarray:
"""Energy supply from `heating_device` to `heating_storage` time series, in [kWh]."""
return self.heating_storage.energy_balance.clip(min=0)[:self.time_step + 1]
@property
def energy_from_dhw_device_to_dhw_storage(self) -> np.ndarray:
"""Energy supply from `dhw_device` to `dhw_storage` time series, in [kWh]."""
return self.dhw_storage.energy_balance.clip(min=0)[:self.time_step + 1]
@property
def energy_to_electrical_storage(self) -> np.ndarray:
"""Energy supply from `electrical_device` to building time series, in [kWh]."""
return self.electrical_storage.energy_balance.clip(min=0)[:self.time_step + 1]
@property
def energy_from_cooling_device(self) -> np.ndarray:
"""Energy supply from `cooling_device` to building time series, in [kWh]."""
return self.__energy_from_cooling_device[:self.time_step + 1]
@property
def energy_from_heating_device(self) -> np.ndarray:
"""Energy supply from `heating_device` to building time series, in [kWh]."""
return self.__energy_from_heating_device[:self.time_step + 1]
@property
def energy_from_dhw_device(self) -> np.ndarray:
"""Energy supply from `dhw_device` to building time series, in [kWh]."""
return self.__energy_from_dhw_device[:self.time_step + 1]
@property
def energy_to_non_shiftable_load(self) -> np.ndarray:
"""Energy supply from grid, PV and battery to non shiftable loads, in [kWh]."""
return self.__energy_to_non_shiftable_load[:self.time_step + 1]
@property
def energy_from_cooling_storage(self) -> np.ndarray:
"""Energy supply from `cooling_storage` to building time series, in [kWh]."""
return self.cooling_storage.energy_balance.clip(max=0)[:self.time_step + 1] * -1
@property
def energy_from_heating_storage(self) -> np.ndarray:
"""Energy supply from `heating_storage` to building time series, in [kWh]."""
return self.heating_storage.energy_balance.clip(max=0)[:self.time_step + 1] * -1
@property
def energy_from_dhw_storage(self) -> np.ndarray:
"""Energy supply from `dhw_storage` to building time series, in [kWh]."""
return self.dhw_storage.energy_balance.clip(max=0)[:self.time_step + 1] * -1
@property
def energy_from_electrical_storage(self) -> np.ndarray:
"""Energy supply from `electrical_storage` to building time series, in [kWh]."""
return self.electrical_storage.energy_balance.clip(max=0)[:self.time_step + 1] * -1
@property
def indoor_dry_bulb_temperature(self) -> np.ndarray:
"""dry bulb temperature time series, in [C].
This is the temperature when cooling_device and heating_device are controlled.
"""
return self.energy_simulation.indoor_dry_bulb_temperature[0:self.time_step + 1]
@property
def indoor_dry_bulb_temperature_cooling_set_point(self) -> np.ndarray:
"""Dry bulb temperature cooling set point time series, in [C]."""
return self.energy_simulation.indoor_dry_bulb_temperature_cooling_set_point[0:self.time_step + 1]
@property
def indoor_dry_bulb_temperature_heating_set_point(self) -> np.ndarray:
"""Dry bulb temperature heating set point time series, in [C]."""
return self.energy_simulation.indoor_dry_bulb_temperature_heating_set_point[0:self.time_step + 1]
@property
def comfort_band(self) -> np.ndarray:
"""Occupant comfort band above the `indoor_dry_bulb_temperature_cooling_set_point` and below the `indoor_dry_bulb_temperature_heating_set_point`, in [C]."""
return self.energy_simulation.comfort_band[0:self.time_step + 1]
@property
def occupant_count(self) -> np.ndarray:
"""Building occupant count time series, in [people]."""
return self.energy_simulation.occupant_count[0:self.time_step + 1]
@property
def cooling_demand(self) -> np.ndarray:
"""Space cooling demand to be met by `cooling_device` and/or `cooling_storage` time series, in [kWh]."""
return self.energy_simulation.cooling_demand[0:self.time_step + 1]
@property
def heating_demand(self) -> np.ndarray:
"""Space heating demand to be met by `heating_device` and/or `heating_storage` time series, in [kWh]."""
return self.energy_simulation.heating_demand[0:self.time_step + 1]
@property
def dhw_demand(self) -> np.ndarray:
"""Domestic hot water demand to be met by `dhw_device` and/or `dhw_storage` time series, in [kWh]."""
return self.energy_simulation.dhw_demand[0:self.time_step + 1]
@property
def non_shiftable_load(self) -> np.ndarray:
"""Electricity load that must be met by the grid, or `PV` and/or `electrical_storage` if available time series, in [kWh]."""
return self.energy_simulation.non_shiftable_load[0:self.time_step + 1]
@property
def cooling_device_cop(self) -> np.ndarray:
"""Heat pump `cooling_device` coefficient of performance time series."""
return self.cooling_device.get_cop(self.weather.outdoor_dry_bulb_temperature, heating=False)[
0:self.time_step + 1]
@property
def heating_device_cop(self) -> np.ndarray:
"""Heat pump `heating_device` coefficient of performance or electric heater `heating_device` static technical efficiency time series."""
return self.heating_device.get_cop(self.weather.outdoor_dry_bulb_temperature, heating=True)[
0:self.time_step + 1] \
if isinstance(self.heating_device, HeatPump) else np.zeros(self.time_step + 1, dtype='float32')
@property
def dhw_device_cop(self) -> np.ndarray:
"""Heat pump `dhw_device` coefficient of performance or electric heater `dhw_device` static technical efficiency time series."""
return self.dhw_device.get_cop(self.weather.outdoor_dry_bulb_temperature, heating=True)[0:self.time_step + 1] \
if isinstance(self.dhw_device, HeatPump) else np.zeros(self.time_step + 1, dtype='float32')
@property
def solar_generation(self) -> np.ndarray:
"""`PV` solar generation (negative value) time series, in [kWh]."""
return self.__solar_generation[:self.time_step + 1]
@property
def power_outage_signal(self) -> np.ndarray:
"""Power outage signal time series, in [Yes/No]."""
return self.__power_outage_signal[:self.time_step + 1]
@property
def downward_electrical_flexibility(self) -> float:
"""Available distributed energy resource capacity to satisfy electric loads while considering power outage at current time step.
It is the sum of solar generation and any discharge from electrical storage, less electricity consumption by cooling, heating,
dhw and non-shfitable load devices as well as charging electrical storage. When there is no power outage, the returned value
is `np.inf`.
"""
capacity = abs(self.solar_generation[self.time_step]) - (
self.cooling_device.electricity_consumption[self.time_step]
+ self.heating_device.electricity_consumption[self.time_step]
+ self.dhw_device.electricity_consumption[self.time_step]
+ self.non_shiftable_load_device.electricity_consumption[self.time_step]
+ self.electrical_storage.electricity_consumption[self.time_step]
)
capacity = capacity if self.power_outage else np.inf
message = 'downward_electrical_flexibility must be >= 0.0!' \
f'time step:, {self.time_step}, outage:, {self.power_outage}, capacity:, {capacity},' \
f' solar:, {abs(self.solar_generation[self.time_step])},' \
f' cooling:, {self.cooling_device.electricity_consumption[self.time_step]},' \
f' heating:, {self.heating_device.electricity_consumption[self.time_step]},' \
f'dhw:, {self.dhw_device.electricity_consumption[self.time_step]},' \
f'non-shiftable:, {self.non_shiftable_load_device.electricity_consumption[self.time_step]},' \
f' battery:, {self.electrical_storage.electricity_consumption[self.time_step]}'
assert capacity >= 0.0 or abs(capacity) < TOLERANCE, message
capacity = max(0.0, capacity)
return capacity
@property
def power_outage(self) -> bool:
"""Whether there is power outage at current time step."""
return self.simulate_power_outage and bool(self.__power_outage_signal[self.time_step])
@property
def stochastic_power_outage_model(self) -> PowerOutage:
"""Power outage model class used to generate stochastic power outage signals."""
return self.__stochastic_power_outage_model
@energy_simulation.setter
def energy_simulation(self, energy_simulation: EnergySimulation):
self.__energy_simulation = energy_simulation
@weather.setter
def weather(self, weather: Weather):
self.__weather = weather
@observation_metadata.setter
def observation_metadata(self, observation_metadata: Mapping[str, bool]):
self.__observation_metadata = observation_metadata
@action_metadata.setter
def action_metadata(self, action_metadata: Mapping[str, bool]):
self.__action_metadata = action_metadata
@carbon_intensity.setter
def carbon_intensity(self, carbon_intensity: CarbonIntensity):
if carbon_intensity is None:
self.__carbon_intensity = CarbonIntensity(
np.zeros(self.episode_tracker.simulation_time_steps, dtype='float32'))
else:
self.__carbon_intensity = carbon_intensity
@pricing.setter
def pricing(self, pricing: Pricing):
if pricing is None:
self.__pricing = Pricing(
np.zeros(self.episode_tracker.simulation_time_steps, dtype='float32'),
np.zeros(self.episode_tracker.simulation_time_steps, dtype='float32'),
np.zeros(self.episode_tracker.simulation_time_steps, dtype='float32'),
np.zeros(self.episode_tracker.simulation_time_steps, dtype='float32'),
)
else:
self.__pricing = pricing
@dhw_storage.setter
def dhw_storage(self, dhw_storage: StorageTank):
self.__dhw_storage = StorageTank(0.0) if dhw_storage is None else dhw_storage
@cooling_storage.setter
def cooling_storage(self, cooling_storage: StorageTank):
self.__cooling_storage = StorageTank(0.0) if cooling_storage is None else cooling_storage
@heating_storage.setter
def heating_storage(self, heating_storage: StorageTank):
self.__heating_storage = StorageTank(0.0) if heating_storage is None else heating_storage
@electrical_storage.setter
def electrical_storage(self, electrical_storage: Battery):
self.__electrical_storage = Battery(0.0, 0.0) if electrical_storage is None else electrical_storage
@dhw_device.setter
def dhw_device(self, dhw_device: Union[HeatPump, ElectricHeater]):
self.__dhw_device = ElectricHeater(0.0) if dhw_device is None else dhw_device
@cooling_device.setter
def cooling_device(self, cooling_device: HeatPump):
self.__cooling_device = HeatPump(0.0) if cooling_device is None else cooling_device
@heating_device.setter
def heating_device(self, heating_device: Union[HeatPump, ElectricHeater]):
self.__heating_device = HeatPump(0.0) if heating_device is None else heating_device
@pv.setter
def pv(self, pv: PV):
self.__pv = PV(0.0) if pv is None else pv
@electric_vehicle_chargers.setter
def electric_vehicle_chargers(self, electric_vehicle_chargers: List[Charger]):
self.__electric_vehicle_chargers = electric_vehicle_chargers
@observation_space.setter
def observation_space(self, observation_space: spaces.Box):
self.__observation_space = observation_space
self.non_periodic_normalized_observation_space_limits = self.estimate_observation_space_limits(include_all=True,
periodic_normalization=False)
self.periodic_normalized_observation_space_limits = self.estimate_observation_space_limits(include_all=True,
periodic_normalization=True)
@action_space.setter
def action_space(self, action_space: spaces.Box):
self.__action_space = action_space
@name.setter
def name(self, name: str):
self.__name = self.uid if name is None else name
@observation_space_limit_delta.setter
def observation_space_limit_delta(self, observation_space_limit_delta: float):
self.__observation_space_limit_delta = 0.0 if observation_space_limit_delta is None else observation_space_limit_delta
if hasattr(self, 'observation_space') and self.observation_space is not None:
self.observation_space = self.estimate_observation_space(include_all=False, normalize=False)
else:
pass
@maximum_temperature_delta.setter
def maximum_temperature_delta(self, maximum_temperature_delta: float):
self.__maximum_temperature_delta = 20.0 if maximum_temperature_delta is None else maximum_temperature_delta
if hasattr(self, 'observation_space') and self.observation_space is not None:
self.observation_space = self.estimate_observation_space(include_all=False, normalize=False)
else:
pass
@demand_observation_limit_factor.setter
def demand_observation_limit_factor(self, demand_observation_limit_factor: float):
self.__demand_observation_limit_factor = 2.0 if demand_observation_limit_factor is None else demand_observation_limit_factor
if hasattr(self, 'observation_space') and self.observation_space is not None:
self.observation_space = self.estimate_observation_space(include_all=False, normalize=False)
else:
pass
@stochastic_power_outage_model.setter
def stochastic_power_outage_model(self, stochastic_power_outage_model: PowerOutage):
self.__stochastic_power_outage_model = PowerOutage() if stochastic_power_outage_model is None else stochastic_power_outage_model
@simulate_power_outage.setter
def simulate_power_outage(self, simulate_power_outage: bool):
self.__simulate_power_outage = False if simulate_power_outage is None else simulate_power_outage
@stochastic_power_outage.setter
def stochastic_power_outage(self, stochastic_power_outage: bool):
self.__stochastic_power_outage = False if stochastic_power_outage is None else stochastic_power_outage
@Environment.random_seed.setter
def random_seed(self, seed: int):
Environment.random_seed.fset(self, seed)
self.cooling_device.random_seed = self.random_seed
self.heating_device.random_seed = self.random_seed
self.dhw_device.random_seed = self.random_seed
self.cooling_storage.random_seed = self.random_seed
self.heating_storage.random_seed = self.random_seed
self.electrical_storage.random_seed = self.random_seed
self.pv.random_seed = self.random_seed
@Environment.episode_tracker.setter
def episode_tracker(self, episode_tracker: EpisodeTracker):
Environment.episode_tracker.fset(self, episode_tracker)
self.cooling_device.episode_tracker = self.episode_tracker
self.heating_device.episode_tracker = self.episode_tracker
self.dhw_device.episode_tracker = self.episode_tracker
self.cooling_storage.episode_tracker = self.episode_tracker
self.heating_storage.episode_tracker = self.episode_tracker
self.dhw_storage.episode_tracker = self.episode_tracker
self.electrical_storage.episode_tracker = self.episode_tracker
self.non_shiftable_load_device.episode_tracker = self.episode_tracker
self.pv.episode_tracker = self.episode_tracker
[docs]
def observations(self, include_all: bool = None, normalize: bool = None, periodic_normalization: bool = None,
check_limits: bool = None) -> Mapping[str, float]:
r"""Observations at current time step.
Parameters
----------
include_all: bool, default: False,
Whether to estimate for all observations as listed in `observation_metadata` or only those that are active.
normalize : bool, default: False
Whether to apply min-max normalization bounded between [0, 1].
periodic_normalization: bool, default: False
Whether to apply sine-cosine normalization to cyclic observations including hour, day_type and month.
check_limits: bool, default: False
Whether to check if observations are within observation space and if not, will send output to log describing
out of bounds observations. Useful for agents that will fail if observations fall outside space e.g. RLlib agents.
Returns
-------
observation_space : spaces.Box
Observation low and high limits.
Notes
-----
Lower and upper bounds of net electricity consumption are rough estimates and may not be completely accurate hence,
scaling this observation-variable using these bounds may result in normalized values above 1 or below 0.
"""
normalize = False if normalize is None else normalize
periodic_normalization = False if periodic_normalization is None else periodic_normalization
include_all = False if include_all is None else include_all
check_limits = False if check_limits is None else check_limits
observations = {}
data = self._get_observations_data()
if include_all:
valid_observations = list(data.keys())
else:
valid_observations = self.active_observations
observations = {k: data[k] for k in valid_observations if k in data.keys()}
# Observations for electric_vehicle_chargers
# Connected is 1 and disconnected is 0
if self.electric_vehicle_chargers is not None:
for charger in self.electric_vehicle_chargers: # If present, itrerate to each charger
charger_id = charger.charger_id
charger_key_state = f'charger_{charger_id}_connected_state' # Observations names are composed from the charger unique ID
charger_key_incoming_state = f'charger_{charger_id}_incoming_state'
if charger.connected_electric_vehicle:
observations[
charger_key_state] = 1 # attributes the f'charger_{charger_id}_connected_state' the value of one (connected EV)
obs = charger.connected_electric_vehicle.observations(include_all, normalize, periodic_normalization)
for k, v in obs.items():
observations[
f'charger_{charger_id}_connected_{k}'] = v # for the connected EV several observations are added (according to the observations specified in Electric_Vehicle class
else: # otherwise, when not connected, 0 is given and observations are filled with -1
observations[charger_key_state] = 0
for o in self.observation_metadata:
if f'charger_{charger_id}_connected' in o and o != charger_key_state:
observations[o] = -0.1
# same logic for incoming EV, which states if an EV is routing towards the charger
if charger.incoming_electric_vehicle:
observations[charger_key_incoming_state] = 1
obs = charger.incoming_electric_vehicle.observations(include_all, normalize, periodic_normalization)
for k, v in obs.items():
observations[f'charger_{charger_id}_incoming_{k}'] = v
else:
observations[charger_key_incoming_state] = 0
for o in self.observation_metadata:
if f'charger_{charger_id}_incoming' in o and o != charger_key_incoming_state:
observations[o] = -0.1
unknown_observations = list(set(valid_observations).difference(observations.keys()))
assert len(unknown_observations) == 0, f'Unknown observations: {unknown_observations}'
non_periodic_low_limit, non_periodic_high_limit = self.non_periodic_normalized_observation_space_limits
periodic_low_limit, periodic_high_limit = self.periodic_normalized_observation_space_limits
periodic_observations = self.get_periodic_observation_metadata()
if check_limits:
for k in self.active_observations:
value = observations[k]
lower = non_periodic_low_limit[k]
upper = non_periodic_high_limit[k]
if not lower <= value <= upper:
report = {
'Building': self.name,
'episode': self.episode_tracker.episode,
'time_step': f'{self.time_step + 1}/{self.episode_tracker.episode_time_steps}',
'observation': k,
'value': value,
'lower': lower,
'upper': upper
}
LOGGER.debug(f'Observation outside space limit: {report}')
else:
pass
else:
pass
if periodic_normalization:
observations_copy = {k: v for k, v in observations.items()}
observations = {}
pn = PeriodicNormalization(x_max=0)
for k, v in observations_copy.items():
if k in periodic_observations:
pn.x_max = max(periodic_observations[k])
sin_x, cos_x = v * pn
observations[f'{k}_cos'] = cos_x
observations[f'{k}_sin'] = sin_x
else:
observations[k] = v
else:
pass
if normalize:
nm = Normalize(0.0, 1.0)
for k, v in observations.items():
nm.x_min = periodic_low_limit[k]
nm.x_max = periodic_high_limit[k]
observations[k] = v * nm
else:
pass
return observations
def _get_observations_data(self) -> Mapping[str, Union[float, int]]:
return {
**{
k.lstrip('_'): self.energy_simulation.__getattr__(k.lstrip('_'))[self.time_step]
for k, v in vars(self.energy_simulation).items() if isinstance(v, np.ndarray)
},
**{
k.lstrip('_'): self.weather.__getattr__(k.lstrip('_'))[self.time_step]
for k, v in vars(self.weather).items() if isinstance(v, np.ndarray)
},
**{
k.lstrip('_'): self.pricing.__getattr__(k.lstrip('_'))[self.time_step]
for k, v in vars(self.pricing).items() if isinstance(v, np.ndarray)
},
**{
k.lstrip('_'): self.carbon_intensity.__getattr__(k.lstrip('_'))[self.time_step]
for k, v in vars(self.carbon_intensity).items() if isinstance(v, np.ndarray)
},
'solar_generation':abs(self.solar_generation[self.time_step]),
**{
'cooling_storage_soc':self.cooling_storage.soc[self.time_step],
'heating_storage_soc':self.heating_storage.soc[self.time_step],
'dhw_storage_soc':self.dhw_storage.soc[self.time_step],
'electrical_storage_soc':self.electrical_storage.soc[self.time_step],
},
'cooling_demand': self.__energy_from_cooling_device[self.time_step] + abs(min(self.cooling_storage.energy_balance[self.time_step], 0.0)),
'heating_demand': self.__energy_from_heating_device[self.time_step] + abs(min(self.heating_storage.energy_balance[self.time_step], 0.0)),
'dhw_demand': self.__energy_from_dhw_device[self.time_step] + abs(min(self.dhw_storage.energy_balance[self.time_step], 0.0)),
'net_electricity_consumption': self.net_electricity_consumption[self.time_step],
'cooling_electricity_consumption': self.cooling_electricity_consumption[self.time_step],
'heating_electricity_consumption': self.heating_electricity_consumption[self.time_step],
'dhw_electricity_consumption': self.dhw_electricity_consumption[self.time_step],
'cooling_storage_electricity_consumption': self.cooling_storage_electricity_consumption[self.time_step],
'heating_storage_electricity_consumption': self.heating_storage_electricity_consumption[self.time_step],
'dhw_storage_electricity_consumption': self.dhw_storage_electricity_consumption[self.time_step],
'electrical_storage_electricity_consumption': self.electrical_storage_electricity_consumption[self.time_step],
'cooling_device_efficiency': self.cooling_device.get_cop(self.weather.outdoor_dry_bulb_temperature[self.time_step], heating=False),
'heating_device_efficiency': self.heating_device.get_cop(
self.weather.outdoor_dry_bulb_temperature[self.time_step], heating=True
) if isinstance(self.heating_device, HeatPump) else self.heating_device.efficiency,
'dhw_device_efficiency': self.dhw_device.get_cop(
self.weather.outdoor_dry_bulb_temperature[self.time_step], heating=True
) if isinstance(self.dhw_device, HeatPump) else self.dhw_device.efficiency,
'indoor_dry_bulb_temperature_cooling_set_point': self.energy_simulation.indoor_dry_bulb_temperature_cooling_set_point[self.time_step],
'indoor_dry_bulb_temperature_heating_set_point': self.energy_simulation.indoor_dry_bulb_temperature_heating_set_point[self.time_step],
'indoor_dry_bulb_temperature_cooling_delta': self.energy_simulation.indoor_dry_bulb_temperature[self.time_step] - self.energy_simulation.indoor_dry_bulb_temperature_cooling_set_point[self.time_step],
'indoor_dry_bulb_temperature_heating_delta': self.energy_simulation.indoor_dry_bulb_temperature[self.time_step] - self.energy_simulation.indoor_dry_bulb_temperature_heating_set_point[self.time_step],
'comfort_band': self.energy_simulation.comfort_band[self.time_step],
'occupant_count': self.energy_simulation.occupant_count[self.time_step],
'power_outage': self.__power_outage_signal[self.time_step],
}
[docs]
def apply_actions(self,
cooling_or_heating_device_action: float = None,
cooling_device_action: float = None, heating_device_action: float = None,
cooling_storage_action: float = None, heating_storage_action: float = None,
dhw_storage_action: float = None, electrical_storage_action: float = None,
electric_vehicle_storage_actions: dict = None,
**kwargs
):
r"""Update cooling and heating demand for next timestep and charge/discharge storage devices.
The order of action execution is dependent on polarity of the storage actions. If the electrical
storage is to be discharged, its action is executed first before all other actions. Likewise, if
the storage for an end-use is to be discharged, the storage action is executed before the control
action for the end-use electric device. Discharging the storage devices before fulfilling thermal
and non-shiftable loads ensures that the discharged energy is considered when allocating electricity
consumption to meet building loads. Likewise, meeting building loads before charging storage devices
ensures that comfort is met before attempting to shift loads.
Parameters
----------
cooling_or_heating_device_action : float, default: np.nan
Fraction of `cooling_device` or `heating_device` `nominal_power` to make available. An action
< 0.0 is for the `cooling_device`, while an action > 0.0 is for the `heating_device`.
cooling_device_action : float, default: np.nan
Fraction of `cooling_device` `nominal_power` to make available for space cooling.
heating_device_action : float, default: np.nan
Fraction of `heating_device` `nominal_power` to make available for space heating.
cooling_storage_action : float, default: 0.0
Fraction of `cooling_storage` `capacity` to charge/discharge by.
heating_storage_action : float, default: 0.0
Fraction of `heating_storage` `capacity` to charge/discharge by.
dhw_storage_action : float, default: 0.0
Fraction of `dhw_storage` `capacity` to charge/discharge by.
electrical_storage_action : float, default: 0.0
Fraction of `electrical_storage` `capacity` to charge/discharge by.
electric_vehicle_storage_actions : dict, default: None
A dictionary where keys are charger IDs and values are the fraction of connected EV battery `capacity`
**kwargs
"""
# hvac devices
if 'cooling_or_heating_device' in self.active_actions:
assert 'cooling_device' not in self.active_actions and 'heating_device' not in self.active_actions, \
'cooling_device and heating_device actions must be set to False when cooling_or_heating_device is True.' \
' They will be implicitly set based on the polarity of cooling_or_heating_device.'
cooling_device_action = abs(min(cooling_or_heating_device_action, 0.0))
heating_device_action = abs(max(cooling_or_heating_device_action, 0.0))
else:
assert not ('cooling_device' in self.active_actions and 'heating_device' in self.active_actions), \
'cooling_device and heating_device actions cannot both be set to True to avoid both actions having' \
' values > 0.0 in the same time step. Use cooling_or_heating_device action instead to control' \
' both cooling_device and heating_device in a building.'
cooling_device_action = np.nan if 'cooling_device' not in self.active_actions else cooling_device_action
heating_device_action = np.nan if 'heating_device' not in self.active_actions else heating_device_action
# energy storage devices
cooling_storage_action = 0.0 if 'cooling_storage' not in self.active_actions else cooling_storage_action
heating_storage_action = 0.0 if 'heating_storage' not in self.active_actions else heating_storage_action
dhw_storage_action = 0.0 if 'dhw_storage' not in self.active_actions else dhw_storage_action
electrical_storage_action = 0.0 if 'electrical_storage' not in self.active_actions else electrical_storage_action
# Initialize electric_vehicle_storage_actions dictionary if not provided
if electric_vehicle_storage_actions is None:
electric_vehicle_storage_actions = {charger.charger_id: 0.0 for charger in self.electric_vehicle_chargers}
# set action priority
actions = {
'cooling_demand': (self.update_cooling_demand, (cooling_device_action,)),
'heating_demand': (self.update_heating_demand, (heating_device_action,)),
'cooling_device': (self.update_energy_from_cooling_device, ()),
'cooling_storage': (self.update_cooling_storage, (cooling_storage_action,)),
'heating_device': (self.update_energy_from_heating_device, ()),
'heating_storage': (self.update_heating_storage, (heating_storage_action,)),
'dhw_device': (self.update_energy_from_dhw_device, ()),
'dhw_storage': (self.update_dhw_storage, (dhw_storage_action,)),
'non_shiftable_load': (self.update_non_shiftable_load, ()),
'electrical_storage': (self.update_electrical_storage, (electrical_storage_action,)),
}
priority_list = list(actions.keys())
for charger in self.electric_vehicle_chargers:
action_key = f'electric_vehicle_storage_{charger.charger_id}'
electric_vehicle_action_value = electric_vehicle_storage_actions.get(charger.charger_id, 0.0)
actions[action_key] = (charger.update_connected_electric_vehicle_soc, (electric_vehicle_action_value,))
electric_vehicle_priority_list = [f'electric_vehicle_storage_{charger.charger_id}' for charger in self.electric_vehicle_chargers]
priority_list = priority_list + electric_vehicle_priority_list # the priority lists are merged
if electrical_storage_action < 0.0:
key = 'electrical_storage'
priority_list.remove(key)
priority_list = [key] + priority_list
else:
pass
for key in ['cooling', 'heating', 'dhw']:
storage = f'{key}_storage'
device = f'{key}_device'
if actions[storage][1][0] < 0.0:
storage_ix = priority_list.index(storage)
device_ix = priority_list.index(device)
priority_list[storage_ix] = device
priority_list[device_ix] = storage
else:
pass
for k in priority_list:
func, args = actions[k]
try:
func(*args)
except NotImplementedError:
pass
self.update_variables()
[docs]
def update_cooling_demand(self, action: float):
"""Update space cooling demand for current time step."""
raise NotImplementedError
[docs]
def update_energy_from_cooling_device(self):
r"""Update cooling device electricity consumption and energy tranfer for current time step's cooling demand."""
demand = self.cooling_demand[self.time_step]
temperature = self.weather.outdoor_dry_bulb_temperature[self.time_step]
storage_output = self.energy_from_cooling_storage[self.time_step]
max_electric_power = self.downward_electrical_flexibility
max_device_output = self.cooling_device.get_max_output_power(temperature, heating=False,
max_electric_power=max_electric_power)
self.___demand_limit_check('cooling', demand, max_device_output)
device_output = min(demand - storage_output, max_device_output)
self.__energy_from_cooling_device[self.time_step] = device_output
electricity_consumption = self.cooling_device.get_input_power(device_output, temperature, heating=False)
# LOGGER.debug(
# 'timestep:', self.time_step, 'bldg:', self.name, 'demand:', demand, 'temperature:', temperature,
# 'storage_capacity:', self.cooling_storage.capacity, 'prev_soc:', self.cooling_storage.soc[self.time_step - 1],
# 'curr_soc:', self.cooling_storage.soc[self.time_step], 'storage_output:', storage_output,
# 'max_electric_power:', max_electric_power, 'max_device_output:', max_device_output, 'device_output:',
# device_output, 'consumption:', electricity_consumption
# )
self.___electricity_consumption_polarity_check('cooling', device_output, electricity_consumption)
self.cooling_device.update_electricity_consumption(max(0.0, electricity_consumption))
[docs]
def update_cooling_storage(self, action: float):
r"""Charge/discharge `cooling_storage` for current time step.
Parameters
----------
action: float
Fraction of `cooling_storage` `capacity` to charge/discharge by.
"""
energy = action * self.cooling_storage.capacity
temperature = self.weather.outdoor_dry_bulb_temperature[self.time_step]
if energy > 0.0:
max_electric_power = self.downward_electrical_flexibility
max_output = self.cooling_device.get_max_output_power(temperature, heating=False,
max_electric_power=max_electric_power)
energy = min(max_output, energy)
else:
demand = self.cooling_demand[self.time_step]
energy = max(-demand, energy)
self.cooling_storage.charge(energy)
charged_energy = max(self.cooling_storage.energy_balance[self.time_step], 0.0)
electricity_consumption = self.cooling_device.get_input_power(charged_energy, temperature, heating=False)
self.cooling_device.update_electricity_consumption(electricity_consumption)
[docs]
def update_heating_demand(self, action: float):
"""Update space heating demand for current time step."""
raise NotImplementedError
[docs]
def update_energy_from_heating_device(self):
r"""Update heating device electricity consumption and energy tranfer for current time step's heating demand."""
demand = self.heating_demand[self.time_step]
temperature = self.weather.outdoor_dry_bulb_temperature[self.time_step]
storage_output = self.energy_from_heating_storage[self.time_step]
max_electric_power = self.downward_electrical_flexibility
max_device_output = self.heating_device.get_max_output_power(temperature, heating=True,
max_electric_power=max_electric_power) \
if isinstance(self.heating_device, HeatPump) else self.heating_device.get_max_output_power(
max_electric_power=max_electric_power)
self.___demand_limit_check('heating', demand, max_device_output)
device_output = min(demand - storage_output, max_device_output)
self.__energy_from_heating_device[self.time_step] = device_output
electricity_consumption = self.heating_device.get_input_power(device_output, temperature, heating=True) \
if isinstance(self.heating_device, HeatPump) else self.heating_device.get_input_power(device_output)
self.___electricity_consumption_polarity_check('heating', device_output, electricity_consumption)
self.heating_device.update_electricity_consumption(max(0.0, electricity_consumption))
[docs]
def update_heating_storage(self, action: float):
r"""Charge/discharge `heating_storage` for current time step.
Parameters
----------
action: float
Fraction of `heating_storage` `capacity` to charge/discharge by.
"""
energy = action * self.heating_storage.capacity
temperature = self.weather.outdoor_dry_bulb_temperature[self.time_step]
if energy > 0.0:
max_electric_power = self.downward_electrical_flexibility
max_output = self.heating_device.get_max_output_power(temperature, heating=True,
max_electric_power=max_electric_power) \
if isinstance(self.heating_device, HeatPump) else self.heating_device.get_max_output_power(
max_electric_power=max_electric_power)
energy = min(max_output, energy)
else:
demand = self.heating_demand[self.time_step]
energy = max(-demand, energy)
self.heating_storage.charge(energy)
charged_energy = max(self.heating_storage.energy_balance[self.time_step], 0.0)
electricity_consumption = self.heating_device.get_input_power(charged_energy, temperature, heating=True) \
if isinstance(self.heating_device, HeatPump) else self.heating_device.get_input_power(charged_energy)
self.heating_device.update_electricity_consumption(electricity_consumption)
[docs]
def update_energy_from_dhw_device(self):
r"""Update dhw device electricity consumption and energy tranfer for current time step's dhw demand."""
demand = self.dhw_demand[self.time_step]
temperature = self.weather.outdoor_dry_bulb_temperature[self.time_step]
storage_output = self.energy_from_dhw_storage[self.time_step]
max_electric_power = self.downward_electrical_flexibility
max_device_output = self.dhw_device.get_max_output_power(temperature, heating=True,
max_electric_power=max_electric_power) \
if isinstance(self.dhw_device, HeatPump) else self.dhw_device.get_max_output_power(
max_electric_power=max_electric_power)
self.___demand_limit_check('dhw', demand, max_device_output)
device_output = min(demand - storage_output, max_device_output)
self.__energy_from_dhw_device[self.time_step] = device_output
electricity_consumption = self.dhw_device.get_input_power(device_output, temperature, heating=True) \
if isinstance(self.dhw_device, HeatPump) else self.dhw_device.get_input_power(device_output)
self.___electricity_consumption_polarity_check('dhw', device_output, electricity_consumption)
self.dhw_device.update_electricity_consumption(max(0.0, electricity_consumption))
[docs]
def update_dhw_storage(self, action: float):
r"""Charge/discharge `dhw_storage` for current time step.
Parameters
----------
action: float
Fraction of `dhw_storage` `capacity` to charge/discharge by.
"""
energy = action * self.dhw_storage.capacity
temperature = self.weather.outdoor_dry_bulb_temperature[self.time_step]
if energy > 0.0:
max_electric_power = self.downward_electrical_flexibility
max_output = self.dhw_device.get_max_output_power(temperature, heating=True,
max_electric_power=max_electric_power) \
if isinstance(self.dhw_device, HeatPump) else self.dhw_device.get_max_output_power(
max_electric_power=max_electric_power)
energy = min(max_output, energy)
else:
demand = self.dhw_demand[self.time_step]
energy = max(-demand, energy)
self.dhw_storage.charge(energy)
charged_energy = max(self.dhw_storage.energy_balance[self.time_step], 0.0)
electricity_consumption = self.dhw_device.get_input_power(charged_energy, temperature, heating=True) \
if isinstance(self.dhw_device, HeatPump) else self.dhw_device.get_input_power(charged_energy)
self.dhw_device.update_electricity_consumption(electricity_consumption)
[docs]
def update_non_shiftable_load(self):
r"""Update non shiftable loads electricity consumption for current time step non shiftable load."""
demand = min(self.non_shiftable_load[self.time_step], self.downward_electrical_flexibility)
self.__energy_to_non_shiftable_load[self.time_step] = demand
self.non_shiftable_load_device.update_electricity_consumption(demand)
# ToDo might need to add chargers here
[docs]
def update_electrical_storage(self, action: float):
r"""Charge/discharge `electrical_storage` for current time step.
Parameters
----------
action : float
Fraction of `electrical_storage` `capacity` to charge/discharge by.
"""
energy = min(action * self.electrical_storage.capacity, self.downward_electrical_flexibility)
self.electrical_storage.charge(energy)
def ___demand_limit_check(self, end_use: str, demand: float, max_device_output: float):
message = f'timestep: {self.time_step}, building: {self.name}, outage: {self.power_outage}, demand: {demand},' \
f'output: {max_device_output}, difference: {demand - max_device_output}, check: {demand <= max_device_output},'
assert self.power_outage or demand <= max_device_output or abs(demand - max_device_output) < TOLERANCE, \
f'demand is greater than {end_use}_device max output | {message}'
def ___electricity_consumption_polarity_check(self, end_use: str, device_output: float,
electricity_consumption: float):
message = f'timestep: {self.time_step}, building: {self.name}, device_output: {device_output}, electricity_consumption: {electricity_consumption}'
assert electricity_consumption >= 0.0 or abs(electricity_consumption) < TOLERANCE, \
f'negative electricity consumption for {end_use} demand | {message}'
[docs]
def estimate_observation_space(self, include_all: bool = None, normalize: bool = None) -> spaces.Box:
r"""Get estimate of observation spaces.
Parameters
----------
include_all: bool, default: False,
Whether to estimate for all observations as listed in `observation_metadata` or only those that are active.
normalize : bool, default: False
Whether to apply min-max normalization bounded between [0, 1].
Returns
-------
observation_space : spaces.Box
Observation low and high limits.
"""
normalize = False if normalize is None else normalize
normalized_observation_space_limits = self.estimate_observation_space_limits(include_all=include_all,
periodic_normalization=True)
unnormalized_observation_space_limits = self.estimate_observation_space_limits(include_all=include_all,
periodic_normalization=False)
if normalize:
low_limit, high_limit = normalized_observation_space_limits
low_limit = [0.0] * len(low_limit)
high_limit = [1.0] * len(high_limit)
else:
low_limit, high_limit = unnormalized_observation_space_limits
low_limit = list(low_limit.values())
high_limit = list(high_limit.values())
return spaces.Box(low=np.array(low_limit, dtype='float32'), high=np.array(high_limit, dtype='float32'),
dtype='float32')
[docs]
def estimate_observation_space_limits(self, include_all: bool = None, periodic_normalization: bool = None) -> Tuple[
Mapping[str, float], Mapping[str, float]]:
r"""Get estimate of observation space limits.
Find minimum and maximum possible values of all the observations, which can then be used by the RL agent to scale the observations
and train any function approximators more effectively.
Parameters
----------
include_all: bool, default: False,
Whether to estimate for all observations as listed in `observation_metadata` or only those that are active.
periodic_normalization: bool, default: False
Whether to apply sine-cosine normalization to cyclic observations including hour, day_type and month.
Returns
-------
observation_space_limits : Tuple[Mapping[str, float], Mapping[str, float]]
Observation low and high limits.
Notes
-----
Lower and upper bounds of net electricity consumption are rough estimates and may not be completely accurate hence,
scaling this observation-variable using these bounds may result in normalized values above 1 or below 0. It is also
assumed that devices and storage systems have been sized.
"""
include_all = False if include_all is None else include_all
internal_limit_observations = [
'net_electricity_consumption_without_storage',
'net_electricity_consumption_without_storage_and_partial_load',
'net_electricity_consumption_without_storage_and_partial_load_and_pv'
]
observation_names = list(
self.observation_metadata.keys()) + internal_limit_observations if include_all else self.active_observations
periodic_normalization = False if periodic_normalization is None else periodic_normalization
periodic_observations = self.get_periodic_observation_metadata()
low_limit, high_limit = {}, {}
data = self._get_observation_space_limits_data()
for key in observation_names:
if key == 'net_electricity_consumption':
# assumes devices and storages have been sized
low_limits = data['non_shiftable_load'] - (
+ self.electrical_storage.nominal_power
+ data['solar_generation']
)
high_limits = data['non_shiftable_load'] \
+ self.cooling_device.nominal_power \
+ self.heating_device.nominal_power \
+ self.dhw_device.nominal_power \
+ self.electrical_storage.nominal_power \
- data['solar_generation']
low_limit[key] = min(low_limits.min(), 0.0)
high_limit[key] = high_limits.max()
elif key == 'net_electricity_consumption_without_storage':
low_limit[key] = min(low_limit['net_electricity_consumption'] + self.electrical_storage.nominal_power,
0.0)
high_limit[key] = high_limit['net_electricity_consumption'] - self.electrical_storage.nominal_power
elif key == 'net_electricity_consumption_without_storage_and_partial_load':
low_limit[key] = low_limit['net_electricity_consumption_without_storage']
high_limit[key] = high_limit['net_electricity_consumption_without_storage']
elif key == 'net_electricity_consumption_without_storage_and_partial_load_and_pv':
low_limit[key] = 0.0
high_limits = data['non_shiftable_load'] \
+ self.cooling_device.nominal_power \
+ self.heating_device.nominal_power \
+ self.dhw_device.nominal_power
high_limit[key] = high_limits.max()
elif key in ['cooling_storage_soc', 'heating_storage_soc', 'dhw_storage_soc', 'electrical_storage_soc',
'electric_vehicle_charger_state', 'electric_vehicle_soc']:
low_limit[key] = 0.0
high_limit[key] = 1.0
elif key in ['electric_vehicle_required_soc_departure', 'electric_vehicle_estimated_soc_arrival']:
low_limit[key] = -0.1
high_limit[key] = 1.0
elif key in ['cooling_device_efficiency']:
cop = self.cooling_device.get_cop(data['outdoor_dry_bulb_temperature'], heating=False)
low_limit[key] = min(cop)
high_limit[key] = max(cop)
elif key in ['heating_device_efficiency']:
if isinstance(self.heating_device, HeatPump):
cop = self.heating_device.get_cop(data['outdoor_dry_bulb_temperature'], heating=True)
low_limit[key] = min(cop)
high_limit[key] = max(cop)
else:
low_limit[key] = self.heating_device.efficiency
high_limit[key] = self.heating_device.efficiency
elif 'charger' in key:
if self.electric_vehicle_chargers is not None:
for charger in self.electric_vehicle_chargers:
if key == f'charger_{charger.charger_id}_connected_state' or key == f'charger_{charger.charger_id}_incoming_state':
low_limit[key] = 0
high_limit[key] = 3
elif 'electric_vehicle_charger_state' in key:
low_limit[key] = 0
high_limit[key] = 1
elif any(value in key for value in
['electric_vehicle_departure_time', 'electric_vehicle_estimated_arrival_time']):
low_limit[key] = -0.1
high_limit[key] = 24
elif any(value in key for value in
['electric_vehicle_required_soc_departure', 'electric_vehicle_estimated_soc_arrival',
'electric_vehicle_soc']):
low_limit[key] = -0.1
high_limit[key] = 1.0
elif key in ['dhw_device_efficiency']:
if isinstance(self.dhw_device, HeatPump):
cop = self.dhw_device.get_cop(data['outdoor_dry_bulb_temperature'], heating=True)
low_limit[key] = min(cop)
high_limit[key] = max(cop)
else:
low_limit[key] = self.dhw_device.efficiency
high_limit[key] = self.dhw_device.efficiency
elif key == 'indoor_dry_bulb_temperature':
low_limit[key] = data['indoor_dry_bulb_temperature'].min() - self.maximum_temperature_delta
high_limit[key] = data['indoor_dry_bulb_temperature'].max() + self.maximum_temperature_delta
elif key in ['indoor_dry_bulb_temperature_cooling_delta', 'indoor_dry_bulb_temperature_heating_delta']:
low_limit[key] = -self.maximum_temperature_delta
high_limit[key] = self.maximum_temperature_delta
elif key == 'comfort_band':
low_limit[key] = 0
high_limit[key] = max(data[key])
elif key in ['cooling_demand', 'heating_demand', 'dhw_demand']:
low_limit[key] = 0.0
max_demand = data[key].max()
high_limit[key] = max_demand * self.demand_observation_limit_factor
elif key == 'cooling_electricity_consumption':
low_limit[key] = 0.0
high_limit[key] = self.cooling_device.nominal_power
elif key == 'heating_electricity_consumption':
low_limit[key] = 0.0
high_limit[key] = self.heating_device.nominal_power
elif key == 'dhw_electricity_consumption':
low_limit[key] = 0.0
high_limit[key] = self.dhw_device.nominal_power
elif key == 'cooling_storage_electricity_consumption':
demand = self.energy_simulation.__getattr__(
f'cooling_demand',
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
)
electricity_consumption = self.cooling_device.get_input_power(demand,
data['outdoor_dry_bulb_temperature'],
False)
low_limit[key] = -max(electricity_consumption)
high_limit[key] = self.cooling_device.nominal_power
elif key == 'heating_storage_electricity_consumption':
demand = self.energy_simulation.__getattr__(
f'heating_demand',
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
)
electricity_consumption = self.heating_device.get_input_power(demand,
data['outdoor_dry_bulb_temperature'],
True) \
if isinstance(self.heating_device, HeatPump) else self.heating_device.get_input_power(demand)
low_limit[key] = -max(electricity_consumption)
high_limit[key] = self.heating_device.nominal_power
elif key == 'dhw_storage_electricity_consumption':
demand = self.energy_simulation.__getattr__(
f'dhw_demand',
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
)
electricity_consumption = self.dhw_device.get_input_power(demand, data['outdoor_dry_bulb_temperature'],
True) \
if isinstance(self.dhw_device, HeatPump) else self.dhw_device.get_input_power(demand)
low_limit[key] = -max(electricity_consumption)
high_limit[key] = self.dhw_device.nominal_power
elif key == 'electrical_storage_electricity_consumption':
low_limit[key] = -self.electrical_storage.nominal_power
high_limit[key] = self.electrical_storage.nominal_power
elif key == 'power_outage':
low_limit[key] = 0.0
high_limit[key] = 1.0
elif periodic_normalization and key in periodic_observations:
pn = PeriodicNormalization(max(periodic_observations[key]))
x_sin, x_cos = pn * np.array(list(periodic_observations[key]))
low_limit[f'{key}_cos'], high_limit[f'{key}_cos'] = min(x_cos), max(x_cos)
low_limit[f'{key}_sin'], high_limit[f'{key}_sin'] = min(x_sin), max(x_sin)
elif key == 'occupant_interaction_indoor_dry_bulb_temperature_set_point_delta':
# will get set in the overriding LogisticRegressionOccupantInteractionBuilding._get_observation_space_limits_data
pass
else:
low_limit[key] = min(data[key])
high_limit[key] = max(data[key])
low_limit = {k: v - self.observation_space_limit_delta for k, v in low_limit.items()}
high_limit = {k: v + self.observation_space_limit_delta for k, v in high_limit.items()}
return low_limit, high_limit
def _get_observation_space_limits_data(self) -> Mapping[str, List[Union[float, int]]]:
# Use entire dataset length for space limit estimation
return {
**{k.lstrip('_'): self.energy_simulation.__getattr__(
k.lstrip('_'),
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
) for k in vars(self.energy_simulation)},
'solar_generation':np.array(self.pv.get_generation(self.energy_simulation.__getattr__(
'solar_generation',
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
))),
**{k.lstrip('_'): self.weather.__getattr__(
k.lstrip('_'),
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
) for k in vars(self.weather)},
**{k.lstrip('_'): self.carbon_intensity.__getattr__(
k.lstrip('_'),
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
) for k in vars(self.carbon_intensity)},
**{k.lstrip('_'): self.pricing.__getattr__(
k.lstrip('_'),
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
) for k in vars(self.pricing)},
}
[docs]
def estimate_action_space(self) -> spaces.Box:
r"""Get estimate of action spaces.
Find minimum and maximum possible values of all the actions, which can then be used by the RL agent to scale the selected actions.
Returns
-------
action_space : spaces.Box
Action low and high limits.
Notes
-----
The lower and upper bounds for the `cooling_storage`, `heating_storage` and `dhw_storage` actions are set to (+/-) 1/maximum_demand for each respective end use,
as the energy storage device can't provide the building with more energy than it will ever need for a given time step. .
For example, if `cooling_storage` capacity is 20 kWh and the maximum `cooling_demand` is 5 kWh, its actions will be bounded between -5/20 and 5/20.
These boundaries should speed up the learning process of the agents and make them more stable compared to setting them to -1 and 1.
"""
low_limit, high_limit = [], []
for key in self.active_actions:
if key == 'cooling_or_heating_device':
if self.cooling_device.nominal_power > ZERO_DIVISION_PLACEHOLDER:
low_limit.append(-1.0)
else:
low_limit.append(0.0)
if self.heating_device.nominal_power > ZERO_DIVISION_PLACEHOLDER:
high_limit.append(1.0)
else:
high_limit.append(0.0)
elif key in ['cooling_device', 'heating_device']:
low_limit.append(0.0)
high_limit.append(1.0)
elif 'electric_vehicle_storage' in key:
if self.electric_vehicle_chargers is not None:
for c in self.electric_vehicle_chargers:
if key == f'electric_vehicle_storage_{c.charger_id}':
discharging_limit = 0 if c.max_discharging_power == 0 else -1
high_limit.append(1.0) # For discharging limit
low_limit.append(discharging_limit) # For charging limit
elif 'storage' in key:
if key == 'electrical_storage':
limit = self.electrical_storage.nominal_power / max(self.electrical_storage.capacity,
ZERO_DIVISION_PLACEHOLDER)
else:
if key == 'cooling_storage':
capacity = self.cooling_storage.capacity
power = self.cooling_device.nominal_power
elif key == 'heating_storage':
capacity = self.heating_storage.capacity
power = self.heating_device.nominal_power
elif key == 'dhw_storage':
capacity = self.dhw_storage.capacity
power = self.dhw_device.nominal_power
else:
raise Exception(f'Unknown action: {key}')
limit = power / max(capacity, ZERO_DIVISION_PLACEHOLDER)
limit = min(limit, 1.0)
low_limit.append(-limit)
high_limit.append(limit)
else:
if key == 'cooling_storage':
capacity = self.cooling_storage.capacity
cooling_demand = self.energy_simulation.__getattr__(
'cooling_demand',
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
)
maximum_demand = cooling_demand.max()
elif key == 'heating_storage':
capacity = self.heating_storage.capacity
heating_demand = self.energy_simulation.__getattr__(
'heating_demand',
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
)
maximum_demand = heating_demand.max()
elif key == 'dhw_storage':
capacity = self.dhw_storage.capacity
dhw_demand = self.energy_simulation.__getattr__(
'dhw_demand',
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
)
maximum_demand = dhw_demand.max()
else:
raise Exception(f'Unknown action: {key}')
maximum_demand_ratio = maximum_demand / max(capacity, ZERO_DIVISION_PLACEHOLDER)
try:
low_limit.append(max(-maximum_demand_ratio, -1.0))
high_limit.append(min(maximum_demand_ratio, 1.0))
except ZeroDivisionError:
low_limit.append(-1.0)
high_limit.append(1.0)
return spaces.Box(low=np.array(low_limit, dtype='float32'), high=np.array(high_limit, dtype='float32'),
dtype='float32')
[docs]
def autosize_cooling_device(self, **kwargs):
"""Autosize `cooling_device` `nominal_power` to minimum power needed to always meet `cooling_demand`.
Other Parameters
----------------
**kwargs : dict
Other keyword arguments parsed to `cooling_device` `autosize` function.
"""
demand = self.energy_simulation.__getattr__(
'cooling_demand',
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
)
temperature = self.weather.__getattr__(
'outdoor_dry_bulb_temperature',
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
)
self.cooling_device.nominal_power = self.cooling_device.autosize(temperature, cooling_demand=demand, **kwargs)
[docs]
def autosize_heating_device(self, **kwargs):
"""Autosize `heating_device` `nominal_power` to minimum power needed to always meet `heating_demand`.
Other Parameters
----------------
**kwargs : dict
Other keyword arguments parsed to `heating_device` `autosize` function.
"""
demand = self.energy_simulation.__getattr__(
'heating_demand',
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
)
temperature = self.weather.__getattr__(
'outdoor_dry_bulb_temperature',
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
)
if isinstance(self.heating_device, HeatPump):
self.heating_device.nominal_power = self.heating_device.autosize(temperature, heating_demand=demand,
**kwargs)
else:
self.heating_device.nominal_power = self.heating_device.autosize(demand, **kwargs)
[docs]
def autosize_dhw_device(self, **kwargs):
"""Autosize `dhw_device` `nominal_power` to minimum power needed to always meet `dhw_demand`.
Other Parameters
----------------
**kwargs : dict
Other keyword arguments parsed to `dhw_device` `autosize` function.
"""
demand = self.energy_simulation.__getattr__(
'dhw_demand',
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
)
temperature = self.weather.__getattr__(
'outdoor_dry_bulb_temperature',
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
)
if isinstance(self.dhw_device, HeatPump):
self.dhw_device.nominal_power = self.dhw_device.autosize(temperature, heating_demand=demand, **kwargs)
else:
self.dhw_device.nominal_power = self.dhw_device.autosize(demand, **kwargs)
[docs]
def autosize_cooling_storage(self, **kwargs):
"""Autosize `cooling_storage` `capacity` to minimum capacity needed to always meet `cooling_demand`.
Other Parameters
----------------
**kwargs : dict
Other keyword arguments parsed to `cooling_storage` `autosize` function.
"""
demand = self.energy_simulation.__getattr__(
'cooling_demand',
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
)
self.cooling_storage.capacity = self.cooling_storage.autosize(demand, **kwargs)
[docs]
def autosize_heating_storage(self, **kwargs):
"""Autosize `heating_storage` `capacity` to minimum capacity needed to always meet `heating_demand`.
Other Parameters
----------------
**kwargs : dict
Other keyword arguments parsed to `heating_storage` `autosize` function.
"""
demand = self.energy_simulation.__getattr__(
'heating_demand',
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
)
self.heating_storage.capacity = self.heating_storage.autosize(demand, **kwargs)
[docs]
def autosize_dhw_storage(self, **kwargs):
"""Autosize `dhw_storage` `capacity` to minimum capacity needed to always meet `dhw_demand`.
Other Parameters
----------------
**kwargs : dict
Other keyword arguments parsed to `dhw_storage` `autosize` function.
"""
demand = self.energy_simulation.__getattr__(
'dhw_demand',
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
)
self.dhw_storage.capacity = self.dhw_storage.autosize(demand, **kwargs)
[docs]
def autosize_electrical_storage(self, **kwargs):
"""Autosize `electrical_storage` `capacity`, `nominal_power`, `depth_of_discharge`, `efficiency`,
`loss_coefficient`, and `capacity_loss_coefficient` to meet an estimated average peak demand.
Other Parameters
----------------
**kwargs : dict
Other keyword arguments parsed to `electrical_storage` `autosize` function.
"""
demand = pd.DataFrame(self._estimate_baseline_electricity_consumption(), columns=['value'])
demand['day'] = (demand.index / (self.seconds_per_time_step * 24 / self.seconds_per_time_step)).astype(int)
demand = demand.groupby('day')['value'].max().mean()
self.electrical_storage.capacity, \
self.electrical_storage.nominal_power, \
self.electrical_storage.depth_of_discharge, \
self.electrical_storage.efficiency, \
self.electrical_storage.loss_coefficient, \
self.electrical_storage.capacity_loss_coefficient = self.electrical_storage.autosize(
demand, **kwargs
)
[docs]
def autosize_pv(self, **kwargs):
"""Autosize `pv` `nominal_power` and set `energy_simulation.solar_generation` using sampled PV data from
LBNL's Tracking The Sun dataset.
Other Parameters
----------------
**kwargs : dict
Other keyword arguments parsed to `electrical_storage` `autosize` function.
"""
demand = pd.DataFrame(self._estimate_baseline_electricity_consumption(), columns=['value'])
demand['year'] = (demand.index / (self.seconds_per_time_step * 24 * 365 / self.seconds_per_time_step)).astype(
int)
demand = demand.groupby('year')['value'].sum().mean()
epw_filepath = kwargs.pop('epw_filepath')
self.pv.nominal_power, solar_generation = self.pv.autosize(demand, epw_filepath, **kwargs)
self.energy_simulation.__setattr__('solar_generation', np.array(solar_generation, dtype='float32'))
def _estimate_baseline_electricity_consumption(self) -> np.ndarray:
"""Returns estimated baseline electricity consumption time series for entire simulation period.
The estimate is the sum of estimated cooling, heating, domestic hot water and non-shiftable
load consumption without storage and self-generation flexibility.
Returns
-------
baseline_electricity_consumption: np.ndarray
Estimate time series for simulation period which may be equal to or longer than the current
episode's number of time steps.
"""
cooling_demand = self.energy_simulation.__getattr__(
'cooling_demand',
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
)
heating_demand = self.energy_simulation.__getattr__(
'heating_demand',
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
)
dhw_demand = self.energy_simulation.__getattr__(
'dhw_demand',
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
)
non_shiftable_electricity_consumption = self.energy_simulation.__getattr__(
'non_shiftable_load',
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
)
outdoor_dry_bulb_temperature = self.weather.__getattr__(
'outdoor_dry_bulb_temperature',
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
)
cooling_electricity_consumption = self.cooling_device.get_input_power(cooling_demand,
outdoor_dry_bulb_temperature,
heating=False)
if isinstance(self.heating_device, HeatPump):
heating_electricity_consumption = self.heating_device.get_input_power(heating_demand,
outdoor_dry_bulb_temperature,
heating=True)
else:
heating_electricity_consumption = self.heating_device.get_input_power(heating_demand)
if isinstance(self.dhw_device, HeatPump):
dhw_electricity_consumption = self.dhw_device.get_input_power(dhw_demand, outdoor_dry_bulb_temperature,
heating=True)
else:
dhw_electricity_consumption = self.dhw_device.get_input_power(dhw_demand)
electricity_consumption = cooling_electricity_consumption + heating_electricity_consumption + dhw_electricity_consumption + non_shiftable_electricity_consumption
return electricity_consumption
[docs]
def next_time_step(self):
r"""Advance all energy storage and electric devices and, PV to next `time_step`."""
self.cooling_device.next_time_step()
self.heating_device.next_time_step()
self.dhw_device.next_time_step()
self.non_shiftable_load_device.next_time_step()
self.cooling_storage.next_time_step()
self.heating_storage.next_time_step()
self.dhw_storage.next_time_step()
self.electrical_storage.next_time_step()
self.pv.next_time_step()
if self.electric_vehicle_chargers is not None:
for c in self.electric_vehicle_chargers:
pass
c.next_time_step()
super().next_time_step()
[docs]
def reset(self):
r"""Reset `Building` to initial state."""
# object reset
super().reset()
self.cooling_storage.reset()
self.heating_storage.reset()
self.dhw_storage.reset()
self.electrical_storage.reset()
self.cooling_device.reset()
self.heating_device.reset()
self.dhw_device.reset()
self.non_shiftable_load_device.reset()
self.pv.reset()
if self.electric_vehicle_chargers is not None:
for c in self.electric_vehicle_chargers:
c.reset()
else:
pass
# variable reset
self.reset_dynamic_variables()
self.reset_data_sets()
self.__solar_generation = self.pv.get_generation(self.energy_simulation.solar_generation) * -1
self.__energy_from_cooling_device = self.energy_simulation.cooling_demand.copy()
self.__energy_from_heating_device = self.energy_simulation.heating_demand.copy()
self.__energy_from_dhw_device = self.energy_simulation.dhw_demand.copy()
self.__energy_to_non_shiftable_load = self.energy_simulation.non_shiftable_load.copy()
self.__net_electricity_consumption = np.zeros(self.episode_tracker.episode_time_steps, dtype='float32')
self.__net_electricity_consumption_emission = np.zeros(self.episode_tracker.episode_time_steps, dtype='float32')
self.__net_electricity_consumption_cost = np.zeros(self.episode_tracker.episode_time_steps, dtype='float32')
self.__power_outage_signal = self.reset_power_outage_signal()
self.__chargers_electricity_consumption = np.zeros(self.episode_tracker.episode_time_steps, dtype='float32')
self.update_variables()
[docs]
def reset_power_outage_signal(self) -> np.ndarray:
"""Resets power outage signal time series.
Resets to zeros if `simulate_power_outage` is `False` otherwise, resets to a stochastic time series
if `stochastic_power_outage` is `True` or the time series defined in `energy_simulation.power_outage`.
Returns
-------
power_outage_signal: np.ndarray
Power outage signal time series.
"""
power_outage_signal = np.zeros(self.episode_tracker.episode_time_steps, dtype='float32')
if self.simulate_power_outage:
if self.stochastic_power_outage:
power_outage_signal = self.stochastic_power_outage_model.get_signals(
self.episode_tracker.episode_time_steps,
seconds_per_time_step=self.seconds_per_time_step,
weather=self.weather
)
else:
power_outage_signal = self.energy_simulation.power_outage.copy()
else:
pass
return power_outage_signal
[docs]
def reset_dynamic_variables(self):
"""Resets data file variables that change during control to their initial values."""
pass
[docs]
def reset_data_sets(self):
"""Resets time series data `start_time_step` and `end_time_step` with respect to current episode's time step settings."""
start_time_step = self.episode_tracker.episode_start_time_step
end_time_step = self.episode_tracker.episode_end_time_step
self.energy_simulation.start_time_step = start_time_step
self.weather.start_time_step = start_time_step
self.pricing.start_time_step = start_time_step
self.carbon_intensity.start_time_step = start_time_step
self.energy_simulation.end_time_step = end_time_step
self.weather.end_time_step = end_time_step
self.pricing.end_time_step = end_time_step
self.carbon_intensity.end_time_step = end_time_step
[docs]
def update_variables(self):
"""Update cooling, heating, dhw and net electricity consumption as well as net electricity consumption cost and carbon emissions."""
if self.time_step == 0:
temperature = self.weather.outdoor_dry_bulb_temperature[self.time_step]
# cooling electricity consumption
cooling_demand = self.__energy_from_cooling_device[self.time_step] + self.cooling_storage.energy_balance[
self.time_step]
cooling_electricity_consumption = self.cooling_device.get_input_power(cooling_demand, temperature,
heating=False)
self.cooling_device.update_electricity_consumption(cooling_electricity_consumption)
# heating electricity consumption
heating_demand = self.__energy_from_heating_device[self.time_step] + self.heating_storage.energy_balance[
self.time_step]
if isinstance(self.heating_device, HeatPump):
heating_electricity_consumption = self.heating_device.get_input_power(heating_demand, temperature,
heating=True)
else:
heating_electricity_consumption = self.dhw_device.get_input_power(heating_demand)
self.heating_device.update_electricity_consumption(heating_electricity_consumption)
# dhw electricity consumption
dhw_demand = self.__energy_from_dhw_device[self.time_step] + self.dhw_storage.energy_balance[self.time_step]
if isinstance(self.dhw_device, HeatPump):
dhw_electricity_consumption = self.dhw_device.get_input_power(dhw_demand, temperature, heating=True)
else:
dhw_electricity_consumption = self.dhw_device.get_input_power(dhw_demand)
self.dhw_device.update_electricity_consumption(dhw_electricity_consumption)
# non shiftable load electricity consumption
non_shiftable_load_electricity_consumption = self.__energy_to_non_shiftable_load[self.time_step]
self.non_shiftable_load_device.update_electricity_consumption(non_shiftable_load_electricity_consumption)
# electrical storage
electrical_storage_electricity_consumption = self.electrical_storage.energy_balance[self.time_step]
self.electrical_storage.update_electricity_consumption(electrical_storage_electricity_consumption,
enforce_polarity=False)
else:
pass
building_chargers_total_electricity_consumption = 0
if self.electric_vehicle_chargers is not None:
for c in self.electric_vehicle_chargers:
building_chargers_total_electricity_consumption = building_chargers_total_electricity_consumption + c.electricity_consumption[
self.time_step - 1]
else:
pass
self.__chargers_electricity_consumption[self.time_step] = building_chargers_total_electricity_consumption
# net electricity consumption
net_electricity_consumption = 0.0
if not self.power_outage:
net_electricity_consumption = self.cooling_device.electricity_consumption[self.time_step] \
+ self.heating_device.electricity_consumption[self.time_step] \
+ self.dhw_device.electricity_consumption[self.time_step] \
+ self.non_shiftable_load_device.electricity_consumption[self.time_step] \
+ self.electrical_storage.electricity_consumption[self.time_step] \
+ self.solar_generation[self.time_step] \
+ self.__chargers_electricity_consumption[self.time_step]
else:
pass
self.__net_electricity_consumption[self.time_step] = net_electricity_consumption
# net electriciy consumption cost
self.__net_electricity_consumption_cost[self.time_step] = net_electricity_consumption * \
self.pricing.electricity_pricing[self.time_step]
# net electriciy consumption emission
self.__net_electricity_consumption_emission[self.time_step] = max(0.0, net_electricity_consumption *
self.carbon_intensity.carbon_intensity[
self.time_step])
[docs]
class DynamicsBuilding(Building): # ToDo
r"""Base class for temperature dynamic building.
Parameters
----------
*args: Any
Positional arguments in :py:class:`citylearn.building.Building`.
dynamics: Dynamics
Indoor dry-bulb temperature dynamics model.
ignore_dynamics: bool, default: False
Wether to simulate temperature dynamics at any time step.
Other Parameters
----------------
**kwargs : Any
Other keyword arguments used to initialize :py:class:`citylearn.building.Building` super class.
"""
def __init__(self, *args: Any, dynamics: Dynamics, ignore_dynamics: bool = None, **kwargs: Any):
"""Intialize `DynamicsBuilding`"""
self.dynamics = dynamics
self.ignore_dynamics = False if ignore_dynamics is None else ignore_dynamics
super().__init__(*args, **kwargs)
@property
def simulate_dynamics(self) -> bool:
"""Whether to predict indoor dry-bulb temperature at current `time_step`."""
return not self.ignore_dynamics
@property
def net_electricity_consumption_emission_without_storage_and_partial_load_and_pv(self) -> np.ndarray:
"""Carbon dioxide emmission from `net_electricity_consumption_without_storage_and_partial_load_pv` time series, in [kg_co2]."""
return (
self.carbon_intensity.carbon_intensity[
0:self.time_step + 1] * self.net_electricity_consumption_without_storage_and_partial_load_and_pv
).clip(min=0)
@property
def net_electricity_consumption_cost_without_storage_and_partial_load_and_pv(self) -> np.ndarray:
"""net_electricity_consumption_without_storage_and_partial_load_and_pv` cost time series, in [$]."""
return self.pricing.electricity_pricing[
0:self.time_step + 1] * self.net_electricity_consumption_without_storage_and_partial_load_and_pv
@property
def net_electricity_consumption_without_storage_and_partial_load_and_pv(self) -> np.ndarray:
"""Net electricity consumption in the absence of flexibility provided by storage devices,
partial load cooling and heating devices and self generation time series, in [kWh].
Notes
-----
net_electricity_consumption_without_storage_and_partial_load_and_pv =
`net_electricity_consumption_without_storage_and_partial_load` - `solar_generation`
"""
return self.net_electricity_consumption_without_storage_and_partial_load - self.solar_generation
@property
def net_electricity_consumption_emission_without_storage_and_partial_load(self) -> np.ndarray:
"""Carbon dioxide emmission from `net_electricity_consumption_without_storage_and_partial_load` time series, in [kg_co2]."""
return (self.carbon_intensity.carbon_intensity[
0:self.time_step + 1] * self.net_electricity_consumption_without_storage_and_partial_load).clip(min=0)
@property
def net_electricity_consumption_cost_without_storage_and_partial_load(self) -> np.ndarray:
"""`net_electricity_consumption_without_storage_and_partial_load` cost time series, in [$]."""
return self.pricing.electricity_pricing[
0:self.time_step + 1] * self.net_electricity_consumption_without_storage_and_partial_load
@property
def net_electricity_consumption_without_storage_and_partial_load(self):
"""Net electricity consumption in the absence of flexibility provided by
storage devices and partial load cooling and heating devices time series, in [kWh]."""
# cooling electricity consumption
cooling_demand_difference = self.cooling_demand_without_partial_load - self.cooling_demand
cooling_electricity_consumption_difference = self.cooling_device.get_input_power(
cooling_demand_difference,
self.weather.outdoor_dry_bulb_temperature[0:self.time_step + 1],
heating=False
)
# heating electricity consumption
heating_demand_difference = self.heating_demand_without_partial_load - self.heating_demand
if isinstance(self.heating_device, HeatPump):
heating_electricity_consumption_difference = self.heating_device.get_input_power(
heating_demand_difference,
self.weather.outdoor_dry_bulb_temperature[self.time_step],
heating=True
)
else:
heating_electricity_consumption_difference = self.dhw_device.get_input_power(heating_demand_difference)
# net electricity consumption without storage and partial load
return self.net_electricity_consumption_without_storage + np.sum([
cooling_electricity_consumption_difference,
heating_electricity_consumption_difference,
], axis=0)
@property
def heating_demand_without_partial_load(self) -> np.ndarray:
"""Total building space ideal heating demand time series in [kWh].
This is the demand when heating_device is not controlled and always supplies ideal load.
"""
return self.energy_simulation.heating_demand_without_control[0:self.time_step + 1]
@property
def cooling_demand_without_partial_load(self) -> np.ndarray:
"""Total building space ideal cooling demand time series in [kWh].
This is the demand when cooling_device is not controlled and always supplies ideal load.
"""
return self.energy_simulation.cooling_demand_without_control[0:self.time_step + 1]
@property
def indoor_dry_bulb_temperature_without_partial_load(self) -> np.ndarray:
"""Ideal load dry bulb temperature time series in [C].
This is the temperature when cooling_device and heating_device
are not controlled and always supply ideal load.
"""
return self.energy_simulation.indoor_dry_bulb_temperature_without_control[0:self.time_step + 1]
[docs]
def apply_actions(self, **kwargs):
super().apply_actions(**kwargs)
self._update_dynamics_input()
if self.simulate_dynamics:
self.update_indoor_dry_bulb_temperature()
else:
pass
[docs]
def update_indoor_dry_bulb_temperature(self):
raise NotImplementedError
def _update_dynamics_input(self):
raise NotImplementedError
[docs]
def reset_dynamic_variables(self):
"""Resets data file variables that change during control to their initial values.
Resets cooling demand, heating deamand and indoor temperature time series to their initial value
at the beginning of an episode.
"""
start_ix = 0
end_ix = self.episode_tracker.episode_time_steps
self.energy_simulation.cooling_demand[
start_ix:end_ix] = self.energy_simulation.cooling_demand_without_control.copy()[start_ix:end_ix]
self.energy_simulation.heating_demand[
start_ix:end_ix] = self.energy_simulation.heating_demand_without_control.copy()[start_ix:end_ix]
self.energy_simulation.indoor_dry_bulb_temperature[
start_ix:end_ix] = self.energy_simulation.indoor_dry_bulb_temperature_without_control.copy()[start_ix:end_ix]
[docs]
def reset(self):
"""Reset Building to initial state and resets `dynamics`."""
super().reset()
self.dynamics.reset()
[docs]
class LSTMDynamicsBuilding(DynamicsBuilding):
r"""Class for building with LSTM temperature dynamics model.
Parameters
----------
*args: Any
Positional arguments in :py:class:`citylearn.building.Building`.
dynamics: Dynamics
Indoor dry-bulb temperature dynamics model.
Other Parameters
----------------
**kwargs : Any
Other keyword arguments used to initialize :py:class:`citylearn.building.Building` super class.
"""
def __init__(self, *args, dynamics: LSTMDynamics, **kwargs):
super().__init__(*args, dynamics=dynamics, **kwargs)
self.dynamics: LSTMDynamics
@DynamicsBuilding.simulate_dynamics.getter
def simulate_dynamics(self) -> bool:
return super().simulate_dynamics and self.dynamics._model_input[0][0] is not None
[docs]
def update_indoor_dry_bulb_temperature(self):
"""Predict and update indoor dry-bulb temperature for current `time_step`.
This method will first apply min-max normalization to the model input data where the input data
is made up of building and district level observations including the predicted
:py:attr:`citylearn.building.Building.energy_simulation.indoor_dry_bulb_temperature`
with all input variables having a length of :py:attr:`citylearn.dynamics.LSTMDynamics.lookback`.
asides the `indoor_dry_bulb_temperature` whose input includes all values from
`time_step` - (`lookback` + 1) to `time_step` - 1, other input variables have values from
`time_step` - `lookback` to `time_step`. The `indoor_dry_bulb_temperature` for the current `time_step`
is then predicted using the input data and current `hidden_state` and the predicted values replaces the
current `time_step` value in :py:attr:`citylearn.building.Building.energy_simulation.indoor_dry_bulb_temperature`.
Notes
-----
LSTM model only uses either cooling/heating demand not both as input variable.
Use :py:attr:`citylearn.building.Building.energy_simulation.hvac_mode` to specify whether to consider cooling
or heating demand at each `time_step`.
"""
# predict
model_input_tensor = torch.tensor(self.get_dynamics_input().T)
model_input_tensor = model_input_tensor[np.newaxis, :, :]
hidden_state = tuple([h.data for h in self.dynamics._hidden_state])
indoor_dry_bulb_temperature_norm, self.dynamics._hidden_state = self.dynamics(model_input_tensor.float(),
hidden_state)
# update dry bulb temperature for current time step in model input
ix = self.dynamics.input_observation_names.index('indoor_dry_bulb_temperature')
self.dynamics._model_input[ix][-1] = indoor_dry_bulb_temperature_norm.item()
# unnormalize temperature
low_limit, high_limit = self.dynamics.input_normalization_minimum[ix], self.dynamics.input_normalization_maximum[ix]
indoor_dry_bulb_temperature = indoor_dry_bulb_temperature_norm*(high_limit - low_limit) + low_limit
# update temperature
# this function is called after advancing to next timestep
# so the cooling demand update and this temperature update are set at the same time step
self.energy_simulation.indoor_dry_bulb_temperature[self.time_step] = indoor_dry_bulb_temperature.item()
def _update_dynamics_input(self):
"""Updates and returns the input time series for the dynmaics prediction model.
Updates the model input with the input variables for the current time step.
The variables in the input will have length of lookback + 1.
"""
# get relevant observations for the current time step
observations = self.observations(include_all=True, normalize=False, periodic_normalization=True)
# append current time step observations to model input
# leave out the oldest set of observations and keep only the previous n
# where n is the lookback + 1 (to include current time step observations)
self.dynamics._model_input = [
l[-self.dynamics.lookback:] + [(observations[k] - min_) / (max_ - min_)]
for l, k, min_, max_ in zip(
self.dynamics._model_input,
self.dynamics.input_observation_names,
self.dynamics.input_normalization_minimum,
self.dynamics.input_normalization_maximum
)
]
[docs]
def update_cooling_demand(self, action: float):
"""Update space cooling demand for current time step.
Sets the value of :py:attr:`citylearn.building.Building.energy_simulation.cooling_demand` for the current `time_step` to
the ouput energy of the cooling device where the proportion of its nominal power made available is defined by `action`.
If :py:attr:`citylearn.building.Building.energy_simulation.hvac_mode` at the next time step is = 0, i.e., off, or = 1,
i.e. cooling mode, the demand is set to 0.
Parameters
----------
action: float
Proportion of cooling device nominal power that is made available.
Notes
-----
Will only start controlling the heat pump when there are enough observations fo the LSTM lookback until then, maintains
ideal load. This will imply that the agent does not learn anything in the initial timesteps that are less than the
lookback. Taking this approach as a 'warm-up' because realistically, there will be no preceding observations to use in
lookback.
"""
# only start controlling the heat pump when there are enough observations fo the LSTM lookback
# until then, maintain ideal load. This will imply that the agent does not learn anything in the
# initial timesteps that are less than the lookback. How does this affect learning longterm?
# Taking this approach as a 'warm-up' because realistically, there will be no preceding observations
# to use in lookback. Alternatively, one can use the rolled observation values at the end of the time series
# but it complicates things and is not too realistic.
if ('cooling_device' in self.active_actions or 'cooling_or_heating_device' in self.active_actions) and self.simulate_dynamics:
if self.energy_simulation.hvac_mode[self.time_step] in [1, 3]:
electric_power = action * self.cooling_device.nominal_power
demand = self.cooling_device.get_max_output_power(
self.weather.outdoor_dry_bulb_temperature[self.time_step],
heating=False,
max_electric_power=electric_power
)
else:
demand = 0.0
self.energy_simulation.cooling_demand[self.time_step] = demand
else:
pass
[docs]
def update_heating_demand(self, action: float):
"""Update space heating demand for current time step.
Sets the value of :py:attr:`citylearn.building.Building.energy_simulation.heating_demand` for the current `time_step` to
the ouput energy of the heating device where the proportion of its nominal power made available is defined by `action`.
If :py:attr:`citylearn.building.Building.energy_simulation.hvac_mode` at the next time step is = 0, i.e., off, or = 1,
i.e. cooling mode, the demand is set to 0.
Parameters
----------
action: float
Proportion of heating device nominal power that is made available.
Notes
-----
Will only start controlling the heat pump when there are enough observations fo the LSTM lookback until then, maintains
ideal load. This will imply that the agent does not learn anything in the initial timesteps that are less than the
lookback. Taking this approach as a 'warm-up' because realistically, there will be no preceding observations to use in
lookback.
"""
if ('heating_device' in self.active_actions or 'cooling_or_heating_device' in self.active_actions) and self.simulate_dynamics:
if self.energy_simulation.hvac_mode[self.time_step] in [2, 3]:
electric_power = action * self.heating_device.nominal_power
demand = self.heating_device.get_max_output_power(
self.weather.outdoor_dry_bulb_temperature[self.time_step],
heating=True,
max_electric_power=electric_power
) if isinstance(self.heating_device, HeatPump) else self.heating_device.get_max_output_power(
max_electric_power=electric_power)
else:
demand = 0.0
self.energy_simulation.heating_demand[self.time_step] = demand
else:
pass
[docs]
class OccupantInteractionBuilding(DynamicsBuilding):
r"""Base class for temperature dynamic and occupant interaction building.
Parameters
----------
*args: Any
Positional arguments in :py:class:`citylearn.building.Building`.
occupant: Occupant
Occupant thermostat interaction model.
ignore_occupant: bool, default: False
Wether to ignore occupant interaction.
Other Parameters
----------------
**kwargs : Any
Other keyword arguments used to initialize :py:class:`citylearn.building.Building` super class.
"""
def __init__(self, *args: Any, occupant: Occupant = None, ignore_occupant: bool = None, **kwargs: Any):
"""Intialize `OccupantInteractionBuilding`"""
self.occupant = occupant
self.ignore_occupant = False if ignore_occupant is None else ignore_occupant
super().__init__(*args, **kwargs)
@DynamicsBuilding.episode_tracker.setter
def episode_tracker(self, episode_tracker: EpisodeTracker):
DynamicsBuilding.episode_tracker.fset(self, episode_tracker)
self.occupant.episode_tracker = episode_tracker
@DynamicsBuilding.random_seed.setter
def random_seed(self, seed: int):
DynamicsBuilding.random_seed.fset(self, seed)
self.occupant.random_seed = self.random_seed
[docs]
def apply_actions(self, **kwargs):
super().apply_actions(**kwargs)
if self.simulate_dynamics:
self.update_set_points()
else:
pass
[docs]
def update_set_points(self):
"""Update building indoor temperature dry-bulb temperature, humidity, etc setpoint using occupant interaction model."""
raise NotImplementedError
[docs]
def reset_dynamic_variables(self):
"""Resets data file variables that change during control to their initial values.
Resets cooling demand, heating deamand and indoor temperature time series to their initial value
at the beginning of an episode.
"""
start_ix = 0
end_ix = self.episode_tracker.episode_time_steps
self.energy_simulation.indoor_dry_bulb_temperature_cooling_set_point[start_ix:end_ix] = self.energy_simulation.indoor_dry_bulb_temperature_cooling_set_point_without_control.copy()[start_ix:end_ix]
self.energy_simulation.indoor_dry_bulb_temperature_heating_set_point[start_ix:end_ix] = self.energy_simulation.indoor_dry_bulb_temperature_heating_set_point_without_control.copy()[start_ix:end_ix]
[docs]
def next_time_step(self):
super().next_time_step()
self.occupant.next_time_step()
[docs]
def reset(self):
"""Reset Building to initial state and resets `dynamics` and `occupant`."""
super().reset()
self.occupant.reset()
[docs]
class LogisticRegressionOccupantInteractionBuilding(OccupantInteractionBuilding, LSTMDynamicsBuilding):
def __init__(self, *args, occupant: LogisticRegressionOccupant = None, set_point_hold_time_steps: int = None, **kwargs):
super().__init__(*args, occupant=occupant, **kwargs)
self.occupant: LogisticRegressionOccupant
self.__set_point_hold_time_step_counter = None
self.set_point_hold_time_steps = set_point_hold_time_steps
self.occupant_interaction_indoor_dry_bulb_temperature_set_point_delta_summary = []
@property
def set_point_hold_time_steps(self) -> int:
return self.__set_point_hold_time_steps
@set_point_hold_time_steps.setter
def set_point_hold_time_steps(self, value: int):
assert value is None or value >= 0, 'set_point_hold_time_steps must be >= 0'
self.__set_point_hold_time_steps = np.inf if value is None else int(value)
[docs]
def update_set_points(self):
current_cooling_set_point = self.energy_simulation.indoor_dry_bulb_temperature_cooling_set_point[self.time_step]
previous_cooling_set_point = self.energy_simulation.indoor_dry_bulb_temperature_cooling_set_point[self.time_step - 1]
current_heating_set_point = self.energy_simulation.indoor_dry_bulb_temperature_heating_set_point[self.time_step]
previous_heating_set_point = self.energy_simulation.indoor_dry_bulb_temperature_heating_set_point[self.time_step - 1]
hvac_mode = self.energy_simulation.hvac_mode[self.time_step]
if hvac_mode == 1:
current_set_point = current_cooling_set_point
previous_set_point = previous_cooling_set_point
elif hvac_mode == 2:
current_set_point = current_heating_set_point
previous_set_point = previous_heating_set_point
elif hvac_mode == 3:
raise NotImplementedError(
'Setpoint update not implemented for auto hvac mode.'
' Set hvac_mode in citylearn.Building.energy_simulation to 0 (off), 1 (cooling mode) or 2 (heating mode).'
)
else:
pass
current_temperature = self.energy_simulation.indoor_dry_bulb_temperature[self.time_step]
previous_temperature = self.energy_simulation.indoor_dry_bulb_temperature[self.time_step - 1]
interaction_input = current_temperature
delta_input = [[current_set_point, previous_set_point, previous_temperature - previous_set_point]]
model_input = (interaction_input, delta_input)
setpoint_delta = self.occupant.predict(x=model_input)
self.occupant.parameters.occupant_interaction_indoor_dry_bulb_temperature_set_point_delta[self.time_step] = setpoint_delta
if abs(setpoint_delta) > 0.0 and not self.ignore_occupant:
if hvac_mode == 1:
self.energy_simulation.indoor_dry_bulb_temperature_cooling_set_point[self.time_step:] = current_set_point + setpoint_delta
elif hvac_mode == 2:
self.energy_simulation.indoor_dry_bulb_temperature_heating_set_point[self.time_step:] = current_set_point + setpoint_delta
else:
pass
self.__set_point_hold_time_step_counter = self.set_point_hold_time_steps
elif self.__set_point_hold_time_step_counter is None:
pass
else:
self.__set_point_hold_time_step_counter -= 1
# revert back to default setpoint schedule if no occupant interaction in defined window
if self.__set_point_hold_time_step_counter is not None and self.__set_point_hold_time_step_counter == 0:
self.energy_simulation.indoor_dry_bulb_temperature_cooling_set_point[self.time_step + 1:] \
= self.energy_simulation.indoor_dry_bulb_temperature_cooling_set_point_without_control[self.time_step + 1:]
self.energy_simulation.indoor_dry_bulb_temperature_heating_set_point[self.time_step + 1:] \
= self.energy_simulation.indoor_dry_bulb_temperature_heating_set_point_without_control[self.time_step + 1:]
self.__set_point_hold_time_step_counter = None
else:
pass
def _get_observations_data(self) -> Mapping[str, Union[float, int]]:
return {
**super()._get_observations_data(),
**{
k.lstrip('_'): self.occupant.parameters.__getattr__(k.lstrip('_'))[self.time_step]
for k, v in vars(self.occupant.parameters).items() if isinstance(v, np.ndarray)
}
}
def _get_observation_space_limits_data(self) -> Mapping[str, List[Union[float, int]]]:
data = {
**super()._get_observation_space_limits_data(),
**{k.lstrip('_'): self.occupant.parameters.__getattr__(
k.lstrip('_'),
start_time_step=self.episode_tracker.simulation_start_time_step,
end_time_step=self.episode_tracker.simulation_end_time_step
) for k in vars(self.occupant.parameters)},
}
# hacky way to set the limits for the occupant setpoint change delta
delta = max(list(self.occupant.delta_output_map.values()))
data['occupant_interaction_indoor_dry_bulb_temperature_set_point_delta'][0] = delta
data['occupant_interaction_indoor_dry_bulb_temperature_set_point_delta'][-1] = -delta
return data
[docs]
def reset_data_sets(self):
super().reset_data_sets()
start_time_step = self.episode_tracker.episode_start_time_step
end_time_step = self.episode_tracker.episode_end_time_step
self.occupant.parameters.start_time_step = start_time_step
self.occupant.parameters.end_time_step = end_time_step
[docs]
def reset_dynamic_variables(self):
super().reset_dynamic_variables()
start_ix = 0
end_ix = self.episode_tracker.episode_time_steps
delta_summary = np.unique(self.occupant.parameters.occupant_interaction_indoor_dry_bulb_temperature_set_point_delta[start_ix:end_ix], return_counts=True)
self.occupant_interaction_indoor_dry_bulb_temperature_set_point_delta_summary.append([delta_summary[0].tolist(), delta_summary[1].tolist()])
self.occupant.parameters.occupant_interaction_indoor_dry_bulb_temperature_set_point_delta[start_ix:end_ix] =\
self.occupant.parameters.occupant_interaction_indoor_dry_bulb_temperature_set_point_delta_without_control.copy()[start_ix:end_ix]
[docs]
def reset(self):
super().reset()
self.__set_point_hold_time_step_counter = None