TITLE: Enriching Neural Network Training Dataset to Improve Worst-Case Performance Guarantees 

ABSTRACT: Machine learning algorithms, especially Neural Networks (NNs), are a valuable
tool used to approximate non-linear relationships, like the AC-Optimal Power
Flow (AC-OPF), with considerable accuracy -- and achieving a speedup of several
orders of magnitude when deployed for use. Often in power systems literature,
the NNs are trained with a fixed dataset generated prior to the training
process. In this paper, we show that adapting the NN training dataset during
training can improve the NN performance and substantially reduce its worst-case
violations. This paper proposes an algorithm that identifies and enriches the
training dataset with critical datapoints that reduce the worst-case violations
and deliver a neural network with improved worst-case performance guarantees.
We demonstrate the performance of our algorithm in four test power systems,
ranging from 39-buses to 162-buses.
Machine learning algorithms, especially Neural Networks (NNs), are a valuable
tool used to approximate non-linear relationships, like the AC-Optimal Power
Flow (AC-OPF), with considerable accuracy -- and achieving a speedup of several
orders of magnitude when deployed for use. Often in power systems literature,
the NNs are trained with a fixed dataset generated prior to the training
process. In this paper, we show that adapting the NN training dataset during
training can improve the NN performance and substantially reduce its worst-case
violations. This paper proposes an algorithm that identifies and enriches the
training dataset with critical datapoints that reduce the worst-case violations
and deliver a neural network with improved worst-case performance guarantees.
We demonstrate the performance of our algorithm in four test power systems,
ranging from 39-buses to 162-buses.
Enriching Neural Network Training Dataset to Improve Worst-Case Performance Guarantees
http://arxiv.org/abs/2303.13228v1