Using OR + AI to Predict the Optimal Production of Offshore Wind Parks: A Preliminary Study

Martina Fischetti, Marco Fraccaro

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationOptimization and Decision Science: Methodologies and Applications,
Volume217
PublisherSpringer
Publication date2017
Pages203-211
ISBN (Print)9783319673073
DOIs
Publication statusPublished - 2017
EventInternational Conference on Optimization and Decision Science -
Duration: 4 Sep 20177 Sep 2017
http://www.airoconference.it/ods2017/

Conference

ConferenceInternational Conference on Optimization and Decision Science
Period04/09/201707/09/2017
Internet address
SeriesOptimization and Decision Science: Methodologies and Applications
Volume217

Keywords

  • Mathematics
  • Optimization
  • Operations Research/Decision Theory
  • Big Data/Analytics
  • Machine learning
  • Mixed integer linear programming
  • Wind energy

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