Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks

• URL: http://arxiv.org/abs/2006.11029v1
• Date: Fri, 19 Jun 2020 09:19:14 GMT
• Title: Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks
• Authors: Andreas Venzke, Guannan Qu, Steven Low, Spyros Chatzivasileiadis
• Abstract summary: We formulate mixed-integer linear programs to obtain worst-case guarantees for neural network predictions. We show that the worst-case guarantees can be up to one order of magnitude larger than the empirical lower bounds calculated with conventional methods.
• Score: 1.8352113484137629
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces for the first time a framework to obtain provable worst-case guarantees for neural network performance, using learning for optimal power flow (OPF) problems as a guiding example. Neural networks have the potential to substantially reduce the computing time of OPF solutions. However, the lack of guarantees for their worst-case performance remains a major barrier for their adoption in practice. This work aims to remove this barrier. We formulate mixed-integer linear programs to obtain worst-case guarantees for neural network predictions related to (i) maximum constraint violations, (ii) maximum distances between predicted and optimal decision variables, and (iii) maximum sub-optimality. We demonstrate our methods on a range of PGLib-OPF networks up to 300 buses. We show that the worst-case guarantees can be up to one order of magnitude larger than the empirical lower bounds calculated with conventional methods. More importantly, we show that the worst-case predictions appear at the boundaries of the training input domain, and we demonstrate how we can systematically reduce the worst-case guarantees by training on a larger input domain than the domain they are evaluated on.

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