The Informational Approach to Global Optimization in presence of very noisy evaluation results. Application to the optimization of renewable energy integration strategies

TL;DR

This paper presents an informational Bayesian optimization method tailored for very noisy function evaluations, demonstrated on renewable energy integration strategy optimization.

Contribution

It introduces a new approach to select evaluation points considering multiple evaluations to mitigate noise effects in entropy estimation.

Findings

Effective optimization of renewable energy strategies under high noise conditions.

Improved entropy estimation by simulating multiple evaluations at each point.

Demonstrated robustness of the method in practical energy network applications.

Abstract

We consider the problem of global optimization of a function f from very noisy evaluations. We adopt a Bayesian sequential approach: evaluation points are chosen so as to reduce the uncertainty about the position of the global optimum of f, as measured by the entropy of the corresponding random variable (Informational Approach to Global Optimization, Villemonteix et al., 2009). When evaluations are very noisy, the error coming from the estimation of the entropy using conditional simulations becomes non negligible compared to its variations on the input domain. We propose a solution to this problem by choosing evaluation points as if several evaluations were going to be made at these points. The method is applied to the optimization of a strategy for the integration of renewable energies into an electrical distribution network.