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

Martina Fischetti (Guest lecturer)

    Activity: Talks and presentationsConference presentations


    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.
    Period6 Sep 2017
    Event titleInternational Conference on Optimization and Decision Science
    Event typeConference


    • machine learning
    • mixed integer linear programming
    • Wind Energy