Using OR + AI to predict the optimal production of offshore wind parks: a preliminary study

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Martina Fischetti - Guest lecturer

In this paper we propose a new use of Machine Learning together with
Mathematical Optimization. We investigate the question of whether a machine,
trained on a large number of optimized solutions, can accurately estimate the value
of the optimized solution for new instances. We focus on instances of a specific
problem, namely, the offshore wind farm layout optimization problem. In this problem
an offshore site is given, together with the wind statistics and the characteristics
of the turbines that need to be built. The optimization wants to determine the optimal
allocation of turbines to maximize the park power production, taking the mutual interference
between turbines into account. Mixed Integer Programming models and
other state-of-the-art optimization techniques, have been developed to solve this
problem. Starting with a dataset of 2000+ optimized layouts found by the optimizer,
we used supervised learning to estimate the production of new wind parks. Our
results show that Machine Learning is able to well estimate the optimal value of
offshore wind farm layout problems.
6 Sep 2017

Event (Conference)

TitleInternational Conference on Optimization and Decision Science
Abbreviated titleODS2017
Date04/09/201707/09/2017
Website
Location
City

    Keywords

  • machine learning, mixed integer linear programming, Wind Energy

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