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In this work we design and compare different supervised learning algorithmsto compute the cost of Alternating Current Optimal Power Flow (ACOPF). Themotivation for quick calculation of OPF cost outcomes stems from the growingneed of algorithmic-based long-term and medium-term planning methodologies inpower networks. Integrated in a multiple time-horizon coordination framework,we refer to this approximation module as a proxy for predicting short-termdecision outcomes without the need of actual simulation and optimization ofthem. Our method enables fast approximate calculation of OPF cost with lessthan 1% error on average, achieved in run-times that are several orders ofmagnitude lower than of exact computation. Several test-cases such asIEEE-RTS96 are used to demonstrate the efficiency of our approach.

Supervised Learning for Optimal Power Flow as a Real-Time Proxy