Module ml4opf.formulations.ac.model

Base class for ACOPF proxy models

Classes

class ACModel
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OPFModel for ACOPF

Ancestors

Subclasses

Class variables

var problem : ACProblem
var violation : ACViolation

Methods

def evaluate_model(self, reduction: str | None = None, inner_reduction: str | None = None) ‑> dict[str, torch.Tensor]
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Evaluate the model on the test data.

Args

reduction : str, optional
Reduction method for the metrics. Defaults to None. Must be one of "mean", "sum","max", "none". If specified, each value in the returned dictionary will be a scalar. Otherwise, they are arrays of shape (n_test_samples,)
inner_reduction : str, optional
Reduction method for turning metrics calculated per component to per sample. Defaults to None. Must be one of "mean", "sum","max", "none".

Returns

dict[str, Tensor]

Dictionary containing Tensor metrics of the model's performance.

vm_lower: Lower bound on the voltage magnitude.

vm_upper: Upper bound on the voltage magnitude.

pg_lower: Lower bound on the real power generation.

pg_upper: Upper bound on the real power generation.

qg_lower: Lower bound on the reactive power generation.

qg_upper: Upper bound on the reactive power generation.

thrm_1: Thermal limit violation from

thrm_2: Thermal limit violation to

p_balance: Active power balance violation.

q_balance: Reactive power balance violation.

pg_mae: Mean absolute error of the real power generation.

qg_mae: Mean absolute error of the reactive power generation.

vm_mae: Mean absolute error of the voltage magnitude.

va_mae: Mean absolute error of the voltage angle. (if not bus-wise and va not in predictions, skipped)

dva_mae: Mean absolute error of the angle difference. (only if not bus-wise)

obj_mape: Mean absolute percent error of the objective value.

def predict(self, pd: torch.Tensor, qd: torch.Tensor) ‑> dict[str, torch.Tensor]
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Predict the ACOPF primal solution for a given set of loads.

Args

pd : Tensor
Active power demand per load.
qd : Tensor
Reactive power demand per load.

Returns

dict[str, Tensor]

Dictionary containing the predicted primal solution.

pg: Active power generation per generator or per bus.

qg: Reactive power generation per generator or per bus.

vm: Voltage magnitude per bus.

va: Voltage angle per bus.

Inherited members

class PerfectACPModel (problem: ACProblem)
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Returns the ground truth, only works with test data.

Ancestors

Methods

def predict(self, pd: torch.Tensor, qd: torch.Tensor) ‑> dict[str, torch.Tensor]
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Return the ground truth. Only works for self.problem.test_data.

Inherited members