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.
Original language | English |
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Title of host publication | Proceedings of 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids |
Number of pages | 7 |
Publisher | IEEE |
Publication date | 2020 |
ISBN (Print) | 9781728161266 |
DOIs | |
Publication status | Published - 2020 |
Event | 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids - Tempe, United States Duration: 11 Nov 2020 → 13 Nov 2020 https://ieeexplore.ieee.org/xpl/conhome/9302911/proceeding |
Conference
Conference | 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids |
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Country/Territory | United States |
City | Tempe |
Period | 11/11/2020 → 13/11/2020 |
Internet address |