citylearn.data module

class citylearn.data.CarbonIntensity(carbon_intensity: Iterable[float])[source]

Bases: object

Building carbon_intensity data class.

carbon_intensity

Grid carbon emission rate time series in [kg_co2/kWh].

Type: np.array

class citylearn.data.DataSet[source]

Bases: object

static copy(name: str, destination_directory: Optional[Union[str, pathlib.Path]] = None)[source]

static get_names() → List[str][source]

static get_schema(name: str)[source]

class citylearn.data.EnergySimulation(month: Iterable[int], hour: Iterable[int], day_type: Iterable[int], daylight_savings_status: Iterable[int], indoor_dry_bulb_temperature: Iterable[float], average_unmet_cooling_setpoint_difference: Iterable[float], indoor_relative_humidity: Iterable[float], non_shiftable_load: Iterable[float], dhw_demand: Iterable[float], cooling_demand: Iterable[float], heating_demand: Iterable[float], solar_generation: Iterable[float])[source]

Bases: object

Building energy_simulation data class.

month

Month time series value ranging from 1 - 12.

Type: np.array

hour

Hour time series value ranging from 1 - 24.

Type: np.array

day_type

Numeric day of week time series ranging from 1 - 8 where 1 - 7 is Monday - Sunday and 8 is reserved for special days e.g. holiday.

Type: np.array

daylight_savings_status

Daylight saving status time series signal of 0 or 1 indicating inactive or active daylight saving respectively.

Type: np.array

indoor_dry_bulb_temperature

Zone volume-weighted average building dry bulb temperature time series in [C].

Type: np.array

average_unmet_cooling_setpoint_difference

Zone volume-weighted average difference between indoor_dry_bulb_temperature and cooling temperature setpoints time series in [C].

Type: np.array

indoor_relative_humidity

Zone volume-weighted average building relative humidity time series in [%].

Type: np.array

non_shiftable_load

Total building non-shiftable plug and equipment loads time series in [kWh].

Type: np.array

dhw_demand

Total building domestic hot water demand time series in [kWh].

Type: np.array

cooling_demand

Total building space cooling demand time series in [kWh].

Type: np.array

heating_demand

Total building space heating demand time series in [kWh].

Type: np.array

solar_generation

Inverter output per 1 kW of PV system time series in [W/kW].

Type: np.array

class citylearn.data.Pricing(electricity_pricing: Iterable[float], electricity_pricing_predicted_6h: Iterable[float], electricity_pricing_predicted_12h: Iterable[float], electricity_pricing_predicted_24h: Iterable[float])[source]

Bases: object

Building pricing data class.

electricity_pricing

Electricity pricing time series in [$].

Type: np.array

electricity_pricing_predicted_6h

Electricity pricing 6 hours ahead prediction time series in [$].

Type: np.array

electricity_pricing_predicted_12h

Electricity pricing 12 hours ahead prediction time series in [$].

Type: np.array

electricity_pricing_predicted_24h

Electricity pricing 24 hours ahead prediction time series in [$].

Type: np.array

class citylearn.data.Weather(outdoor_dry_bulb_temperature: Iterable[float], outdoor_relative_humidity: Iterable[float], diffuse_solar_irradiance: Iterable[float], direct_solar_irradiance: Iterable[float], outdoor_dry_bulb_temperature_predicted_6h: Iterable[float], outdoor_dry_bulb_temperature_predicted_12h: Iterable[float], outdoor_dry_bulb_temperature_predicted_24h: Iterable[float], outdoor_relative_humidity_predicted_6h: Iterable[float], outdoor_relative_humidity_predicted_12h: Iterable[float], outdoor_relative_humidity_predicted_24h: Iterable[float], diffuse_solar_irradiance_predicted_6h: Iterable[float], diffuse_solar_irradiance_predicted_12h: Iterable[float], diffuse_solar_irradiance_predicted_24h: Iterable[float], direct_solar_irradiance_predicted_6h: Iterable[float], direct_solar_irradiance_predicted_12h: Iterable[float], direct_solar_irradiance_predicted_24h: Iterable[float])[source]

Bases: object

Building weather data class.

outdoor_dry_bulb_temperature

Outdoor dry bulb temperature time series in [C].

Type: np.array

outdoor_relative_humidity

Outdoor relative humidity time series in [%].

Type: np.array

diffuse_solar_irradiance

Diffuse solar irradiance time series in [W/m^2].

Type: np.array

direct_solar_irradiance

Direct solar irradiance time series in [W/m^2].

Type: np.array

outdoor_dry_bulb_temperature_predicted_6h

Outdoor dry bulb temperature 6 hours ahead prediction time series in [C].

Type: np.array

outdoor_dry_bulb_temperature_predicted_12h

Outdoor dry bulb temperature 12 hours ahead prediction time series in [C].

Type: np.array

outdoor_dry_bulb_temperature_predicted_24h

Outdoor dry bulb temperature 24 hours ahead prediction time series in [C].

Type: np.array

outdoor_relative_humidity_predicted_6h

Outdoor relative humidity 6 hours ahead prediction time series in [%].

Type: np.array

outdoor_relative_humidity_predicted_12h

Outdoor relative humidity 12 hours ahead prediction time series in [%].

Type: np.array

outdoor_relative_humidity_predicted_24h

Outdoor relative humidity 24 hours ahead prediction time series in [%].

Type: np.array

diffuse_solar_irradiance_predicted_6h

Diffuse solar irradiance 6 hours ahead prediction time series in [W/m^2].

Type: np.array

diffuse_solar_irradiance_predicted_12h

Diffuse solar irradiance 12 hours ahead prediction time series in [W/m^2].

Type: np.array

diffuse_solar_irradiance_predicted_24h

Diffuse solar irradiance 24 hours ahead prediction time series in [W/m^2].

Type: np.array

direct_solar_irradiance_predicted_6h

Direct solar irradiance 6 hours ahead prediction time series in [W/m^2].

Type: np.array

direct_solar_irradiance_predicted_12h

Direct solar irradiance 12 hours ahead prediction time series in [W/m^2].

Type: np.array

direct_solar_irradiance_predicted_24h

Direct solar irradiance 24 hours ahead prediction time series in [W/m^2].

Type: np.array