citylearn.end_use_load_profiles.neighborhood module

class citylearn.end_use_load_profiles.neighborhood.Neighborhood(weather_data: str = None, year_of_publication: int = None, release: int = None, cache: bool = None, energyplus_output_directory: Path | str = None, dataset_directory: Path | str = None, max_workers: int = None, random_seed: int = None)[source]

Bases: object

build(idd_filepath: Path | str, bldg_ids: List[int] = None, include_lstm_models: bool = None, test_lstm_models: bool = None, test_citylearn_simulation: bool = None, delete_energyplus_simulation_output: bool = None, sample_buildings_kwargs: Mapping[str, Any] = None, energyplus_simulation_kwargs: Mapping[str, Any] = None, train_lstm_kwargs: Mapping[str, Any] = None, schema_kwargs: Mapping[str, Any] = None, test_citylearn_simulation_kwargs: Mapping[str, Any] = None) → NeighborhoodBuild[source]

property dataset_directory: Path | str

delete_energyplus_simulation_output(simulators: List[Mapping[str, EndUseLoadProfilesEnergyPlusSimulator | List[EndUseLoadProfilesEnergyPlusPartialLoadSimulator]]])[source]

property end_use_load_profiles: EndUseLoadProfiles

property energyplus_output_directory: Path | str

get_lstm_training_data(simulators: List[Mapping[str, EndUseLoadProfilesEnergyPlusSimulator | List[EndUseLoadProfilesEnergyPlusPartialLoadSimulator]]]) → List[DataFrame][source]

get_weather_data(simulator: EndUseLoadProfilesEnergyPlusPartialLoadSimulator, shifts: Tuple[int, int, int] = None, accuracy: Mapping[str, Tuple[float, float, float]] = None) → DataFrame[source]

property max_workers: int

multiprocess_test_lstm(training_data: List[DataFrame], schema_filepath: Path) → List[DataFrame][source]

property random_seed: int

sample_buildings(filters: Mapping[str, List[Any]] = None, sample_method: SampleMethod = None, sample_count: int = None, duplicate_to_count: bool = None, single_county: bool = None, single_family_detached: bool = None, **kwargs) → Tuple[List[int], List[int], Mapping[str, Any]][source]

set_schema(simulators: List[Mapping[str, EndUseLoadProfilesEnergyPlusSimulator | List[EndUseLoadProfilesEnergyPlusPartialLoadSimulator]]], bldg_ids: List[int], lstm_models: List[Mapping[str, Any]] = None, template: Mapping[str, dict | float | int | str] = None, metadata: DataFrame = None, dataset_name: str = None, schema_directory: Path | str = None, weather_kwargs: dict = None) → Path[source]

simulate_energy_plus(bldg_ids: List[int], idd_filepath: Path | str, simulation_ids: List[str] = None, models: List[Path | str] = None, schedules: Path | DataFrame = None, osm: bool = None, partial_loads_simulations: int = None, partial_loads_kwargs: Mapping[str, Any] = None, **kwargs) → List[Mapping[str, EndUseLoadProfilesEnergyPlusSimulator | List[EndUseLoadProfilesEnergyPlusPartialLoadSimulator]]][source]

test_citylearn_simulation(schema: Path, model: Agent = None, env_kwargs: Mapping[str, Any] = None, model_kwargs: Mapping[str, Any] = None, report_lstm_performance: bool = None) → Tuple[DataFrame, List[DataFrame], List[DataFrame]][source]

test_lstm(bldg_ix: int, training_data: DataFrame, schema_filepath: Path) → Tuple[int, DataFrame][source]

train_lstm(data: List[DataFrame], config: Mapping[str, Any] = None, seed: int = None) → List[Mapping[str, Any]][source]

class citylearn.end_use_load_profiles.neighborhood.NeighborhoodBuild(schema_filepath: Path, citylearn_simulation_test_evaluation: DataFrame, citylearn_simulation_lstm_prediction_data: Mapping[int, DataFrame], citylearn_simulation_lstm_error_data: Mapping[int, DataFrame], lstm_test_data: Mapping[int, DataFrame], bldg_ids: List[int], sample_cluster_labels: List[int], sample_metadata: Mapping[str, Any], simulators: List[Mapping[str, EndUseLoadProfilesEnergyPlusSimulator | List[EndUseLoadProfilesEnergyPlusPartialLoadSimulator]]])[source]

Bases: object

property bldg_ids: List[int]

property citylearn_simulation_lstm_error_data: List[DataFrame]

property citylearn_simulation_lstm_prediction_data: List[DataFrame]

property citylearn_simulation_test_evaluation: DataFrame

property lstm_test_data: List[DataFrame]

property sample_cluster_labels: List[int]

property sample_metadata: Mapping[str, Any]

property schema_filepath: Path

property simulators: List[Mapping[str, EndUseLoadProfilesEnergyPlusSimulator | List[EndUseLoadProfilesEnergyPlusPartialLoadSimulator]]]

class citylearn.end_use_load_profiles.neighborhood.SampleMethod(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]

Bases: Enum

METADATA_CLUSTER_FREQUENCY = 1

RANDOM = 0