flexmeasures.data.services.scheduling_result

Classes

class flexmeasures.data.services.scheduling_result.SchedulingJobResult(unresolved: list = <factory>, resolved: list = <factory>, outputs: list = <factory>, num_beliefs: int = 0)

Constraint analysis results from a scheduling job.

Holds soft state-of-charge constraint analysis (unmet and satisfied targets) produced by the scheduler when optimizing storage devices. Results are available exclusively via GET /api/v3_0/jobs/<uuid> in the result field.

The sensor schedule endpoint (GET /api/v3_0/sensors/<id>/schedules/<job_id>) returns power values only and does not include constraint analysis.

Structure: Results contain four top-level fields:

• unresolved: List of soft constraints that the scheduler could not satisfy

  • Each entry is a dict with "asset" field (asset ID) and constraint-type keys ("soc-minima", "soc-maxima")

  • Each constraint-type key holds a list of dicts, one per violated slot (chronologically ordered): {"datetime": ISO 8601 UTC, "violation": "X kWh"}

• resolved: List of soft constraints that were satisfied with available headroom

  • Each entry is a dict with "asset" field and constraint-type keys

  • Each constraint-type key holds a list of dicts, one per met slot (chronologically ordered): {"datetime": ISO 8601 UTC, "margin": "X kWh"}

• outputs: List of the sensors the schedule records on, one entry per sensor

  • Each entry is a dict with "sensor" field (sensor ID), "num-computed-beliefs" (the beliefs the scheduler computed for it), and "num-recorded-beliefs" (those recorded, which leaves out beliefs that repeat what is already on record or have no value).

  • On a dry run, nothing is recorded, so each entry’s "num-recorded-beliefs" is 0.

• num-beliefs: Total number of beliefs (scheduled values) computed, i.e. the total of "num-computed-beliefs" across outputs.

Important: soc-targets (hard constraints) are never included since they are strictly enforced by the scheduler. Only hard constraint failures cause job failure.

Example:

{
    "unresolved": [
        {
            "asset": 42,
            "soc-minima": [
                {"datetime": "2024-01-01T10:00:00+00:00", "violation": "260.0 kWh"},
                {"datetime": "2024-01-01T10:15:00+00:00", "violation": "180.0 kWh"},
            ],
        }
    ],
    "resolved": [
        {
            "asset": 42,
            "soc-maxima": [
                {"datetime": "2024-01-01T12:00:00+00:00", "margin": "40.0 kWh"},
            ],
        }
    ],
    "outputs": [
        {"sensor": 5, "num-computed-beliefs": 96, "num-recorded-beliefs": 96},
    ],
    "num-beliefs": 96
}

For usage examples and interpretation guidance, see Accessing constraint results.

__init__(unresolved: list = <factory>, resolved: list = <factory>, outputs: list = <factory>, num_beliefs: int = 0) → None

classmethod from_dict(d: dict) → SchedulingJobResult

Deserialize from a dict.

to_dict() → dict

Serialize to a JSON-compatible dict.