sinergym.envs.eplus_env.EplusEnv

class sinergym.envs.eplus_env.EplusEnv(building_file: str, weather_files: str | ~typing.List[str], action_space: ~gymnasium.spaces.box.Box = Box([], [], (0,), float32), time_variables: ~typing.List[str] = [], variables: ~typing.Dict[str, ~typing.Tuple[str, str]] = {}, meters: ~typing.Dict[str, str] = {}, actuators: ~typing.Dict[str, ~typing.Tuple[str, str, str]] = {}, weather_variability: ~typing.Tuple[float, float, float] | None = None, reward: ~typing.Any = <class 'sinergym.utils.rewards.LinearReward'>, reward_kwargs: ~typing.Dict[str, ~typing.Any] | None = {}, max_ep_data_store_num: int = 10, env_name: str = 'eplus-env-v1', config_params: ~typing.Dict[str, ~typing.Any] | None = None)

__init__(building_file: str, weather_files: str | ~typing.List[str], action_space: ~gymnasium.spaces.box.Box = Box([], [], (0,), float32), time_variables: ~typing.List[str] = [], variables: ~typing.Dict[str, ~typing.Tuple[str, str]] = {}, meters: ~typing.Dict[str, str] = {}, actuators: ~typing.Dict[str, ~typing.Tuple[str, str, str]] = {}, weather_variability: ~typing.Tuple[float, float, float] | None = None, reward: ~typing.Any = <class 'sinergym.utils.rewards.LinearReward'>, reward_kwargs: ~typing.Dict[str, ~typing.Any] | None = {}, max_ep_data_store_num: int = 10, env_name: str = 'eplus-env-v1', config_params: ~typing.Dict[str, ~typing.Any] | None = None)

Environment with EnergyPlus simulator.

Parameters:

building_file (str) – Name of the JSON file with the building definition.
weather_files (Union[str,List[str]]) – Name of the EPW file for weather conditions. It can be specified a list of weathers files in order to sample a weather in each episode randomly.
action_space (gym.spaces.Box, optional) – Gym Action Space definition. Defaults to an empty action_space (no control).
time_variables (List[str]) – EnergyPlus time variables we want to observe. The name of the variable must match with the name of the E+ Data Transfer API method name. Defaults to empty list.
variables (Dict[str, Tuple[str, str]]) – Specification for EnergyPlus Output:Variable. The key name is custom, then tuple must be the original variable name and the output variable key. Defaults to empty dict.
meters (Dict[str, str]) – Specification for EnergyPlus Output:Meter. The key name is custom, then value is the original EnergyPlus Meters name.
actuators (Dict[str, Tuple[str, str, str]]) – Specification for EnergyPlus Input Actuators. The key name is custom, then value is a tuple with actuator type, value type and original actuator name. Defaults to empty dict.
weather_variability (Optional[Tuple[float]], optional) – Tuple with sigma, mu and tao of the Ornstein-Uhlenbeck process to be applied to weather data. Defaults to None.
reward (Any, optional) – Reward function instance used for agent feedback. Defaults to LinearReward.
reward_kwargs (Optional[Dict[str, Any]], optional) – Parameters to be passed to the reward function. Defaults to empty dict.
max_ep_data_store_num (int, optional) – Number of last sub-folders (one for each episode) generated during execution on the simulation.
env_name (str, optional) – Env name used for working directory generation. Defaults to eplus-env-v1.
config_params (Optional[Dict[str, Any]], optional) – Dictionary with all extra configuration for simulator. Defaults to None.

Methods

`__init__`(building_file, weather_files[, ...])	Environment with EnergyPlus simulator.
`close`()	End simulation.
`get_wrapper_attr`(name)	Gets the attribute name from the environment.
`has_wrapper_attr`(name)	Checks if the attribute name exists in the environment.
`info`()
`render`([mode])	Environment rendering.
`reset`([seed, options])	Reset the environment.
`set_wrapper_attr`(name, value)	Sets the attribute name on the environment with value.
`step`(action)	Sends action to the environment.

Attributes

`action_space`
`actuator_handlers`
`available_handlers`
`building_path`
`ddy_path`
`episode_length`
`episode_path`
`idd_path`
`is_discrete`
`is_running`
`logger`
`metadata`
`meter_handlers`
`np_random`	Returns the environment's internal `_np_random` that if not set will initialise with a random seed.
`np_random_seed`	Returns the environment's internal `_np_random_seed` that if not set will first initialise with a random int as seed.
`observation_space`
`render_mode`
`runperiod`
`schedulers`
`spec`
`step_size`
`timestep_per_episode`
`unwrapped`	Returns the base non-wrapped environment.
`var_handlers`
`weather_path`
`workspace_path`
`zone_names`

property action_space: Space[Any]

property actuator_handlers: Dict[str, int] | None

property available_handlers: str | None

property building_path: str

close() → None: End simulation.

property ddy_path: str

property episode_length: float

property episode_path: str

property idd_path: str

info()

property is_discrete: bool

property is_running: bool

logger = <Logger ENVIRONMENT (INFO)>

metadata: dict[str, Any] = {'render_modes': ['human']}

property meter_handlers: Dict[str, int] | None

property observation_space: Space[Any]

render(mode: str = 'human') → None

Environment rendering.

Parameters:: mode (str, optional) – Mode for rendering. Defaults to ‘human’.

reset(seed: int | None = None, options: Dict[str, Any] | None = None) → Tuple[ndarray, Dict[str, Any]]

Reset the environment.

Parameters:

seed (Optional[int]) – The seed that is used to initialize the environment’s episode (np_random). if value is None, a seed will be chosen from some source of entropy. Defaults to None.
options (Optional[Dict[str, Any]]) – Additional information to specify how the environment is reset. Defaults to None.

Returns:

Current observation and info context with additional information.

Return type:

Tuple[np.ndarray,Dict[str,Any]]

property runperiod: Dict[str, int]

property schedulers: Dict[str, Dict[str, str | Dict[str, str]]]

Sends action to the environment.

Parameters:: action (Union[int, float, np.integer, np.ndarray, List[Any], Tuple[Any]]) – Action selected by the agent.
Returns:: Observation for next timestep, reward obtained, Whether the episode has ended or not, Whether episode has been truncated or not, and a dictionary with extra information
Return type:: Tuple[np.ndarray, float, bool, Dict[str, Any]]

property step_size: float

property timestep_per_episode: int

property var_handlers: Dict[str, int] | None

property weather_path: str

property workspace_path: str

property zone_names: list