agentlib_mpc.modules.InputPrediction package

Submodules

agentlib_mpc.modules.InputPrediction.try_predictor module

class agentlib_mpc.modules.InputPrediction.try_predictor.TRYPredictor(*, config: dict, agent: Agent)[source]

Bases: TRYSensor

process()[source]: Write the current TRY values into data_broker every other t_sample

send_measurement()[source]

send_prediction()[source]

pydantic model agentlib_mpc.modules.InputPrediction.try_predictor.TRYPredictorConfig[source]

Bases: TRYSensorConfig

Config:

arbitrary_types_allowed: bool = True
validate_assignment: bool = True
extra: str = forbid
frozen: bool = True

Fields:

prediction_length (int)
predictions (List[agentlib.core.datamodels.AgentVariable])

Validators:

field prediction_length: int = 24: prediction length in hours

field predictions: List[AgentVariable] = [AgentVariable(name='T_oda_prediction', type=None, timestamp=None, unit='K', description='Air temperature 2m over ground [K]', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='T_oda_prediction', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='pressure_prediction', type=None, timestamp=None, unit='hPa', description='Air pressure in standard height [hPa]', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='pressure_prediction', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='wind_direction_prediction', type=None, timestamp=None, unit='°', description='Wind direction 10 m above gorund [Grad] {0..360;999}', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='wind_direction_prediction', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='wind_speed_prediction', type=None, timestamp=None, unit='m/s', description='Wind speed 10 m above ground [m/s]', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='wind_speed_prediction', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='coverage_prediction', type=None, timestamp=None, unit='eighth', description='[eighth] {0..8;9}', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='coverage_prediction', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='absolute_humidity_prediction', type=None, timestamp=None, unit='g/kg', description='[g/kg]', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='absolute_humidity_prediction', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='relative_humidity_prediction', type=None, timestamp=None, unit='%', description='Relative humidity 2 m above ground [%] {1..100}', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='relative_humidity_prediction', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='beam_direct_prediction', type=None, timestamp=None, unit='W/m^2', description='Direct beam of sun (hor. plane) [W/m^2] downwards: positive', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='beam_direct_prediction', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='beam_diffuse_prediction', type=None, timestamp=None, unit='/m^2', description='Diffuse beam of sun (hor. plane) [W/m^2] downwards: positive', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='beam_diffuse_prediction', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='beam_atm_prediction', type=None, timestamp=None, unit='/m^2', description='Beam of atmospheric heat (hor. plane) [W/m^2] downwards: positive', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='beam_atm_prediction', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None), AgentVariable(name='beam_terr_prediction', type=None, timestamp=None, unit='/m^2', description='Beam of terrestrial heat [W/m^2] upwards: negative', ub=inf, lb=-inf, clip=False, allowed_values=[], value=None, alias='beam_terr_prediction', source=Source(agent_id=None, module_id=None), shared=None, rdf_class=None)]

model_post_init(context: Any, /) → None: We need to both initialize private attributes and call the user-defined model_post_init method.