### Abstract

Original language | English |
---|---|

Journal | Computers & Operations Research |

Number of pages | 27 |

ISSN | 0305-0548 |

DOIs | |

Publication status | Published - 2018 |

### Keywords

- Machine Learning
- Mixed Integer Linear Programming
- Wind Farm Optimization
- Green Energy

### Cite this

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**Machine Learning meets Mathematical Optimization to predict the optimal production of offshore wind parks.** / Fischetti, Martina; Fraccaro, Marco.

Research output: Contribution to journal › Journal article › Research › peer-review

TY - JOUR

T1 - Machine Learning meets Mathematical Optimization to predict the optimal production of offshore wind parks

AU - Fischetti, Martina

AU - Fraccaro, Marco

PY - 2018

Y1 - 2018

N2 - In this paper we propose a combination of Mathematical Optimization and Machine Learning to estimate the value of optimized solutions. In particular, we investigate if a machine, trained on a large number of optimized solutions, could accurately estimate the value of the optimized solution for new instances. In this paper we will focus on a specific application: the offshore wind farm layout optimization problem. Mixed Integer Programming models and other state-of-the-art optimization techniques, have been developed to solve this problem. Given the complexity of the problem and the big difference in production between optimized/non optimized solutions, it is not trivial to understand the potential value of a new site without running a complete optimization. This could be too time consuming if a lot of sites need to be evaluated, therefore we propose to use Machine Learning to quickly estimate the potential of new sites (i.e., to estimate the optimized production of a site without explicitly running the optimization). To do so, we trained and tested different Machine Learning models on a dataset of 3000+ optimized layouts found by the optimizer. Thanks to the close collaboration with a leading company in the energy sector, our model was trained on real-world data. Our results show that Machine Learning is able to efficiently estimate the value of optimized instances for the offshore wind farm layout problem

AB - In this paper we propose a combination of Mathematical Optimization and Machine Learning to estimate the value of optimized solutions. In particular, we investigate if a machine, trained on a large number of optimized solutions, could accurately estimate the value of the optimized solution for new instances. In this paper we will focus on a specific application: the offshore wind farm layout optimization problem. Mixed Integer Programming models and other state-of-the-art optimization techniques, have been developed to solve this problem. Given the complexity of the problem and the big difference in production between optimized/non optimized solutions, it is not trivial to understand the potential value of a new site without running a complete optimization. This could be too time consuming if a lot of sites need to be evaluated, therefore we propose to use Machine Learning to quickly estimate the potential of new sites (i.e., to estimate the optimized production of a site without explicitly running the optimization). To do so, we trained and tested different Machine Learning models on a dataset of 3000+ optimized layouts found by the optimizer. Thanks to the close collaboration with a leading company in the energy sector, our model was trained on real-world data. Our results show that Machine Learning is able to efficiently estimate the value of optimized instances for the offshore wind farm layout problem

KW - Machine Learning

KW - Mixed Integer Linear Programming

KW - Wind Farm Optimization

KW - Green Energy

U2 - 10.1016/j.cor.2018.04.006

DO - 10.1016/j.cor.2018.04.006

M3 - Journal article

JO - Computers & Operations Research

JF - Computers & Operations Research

SN - 0305-0548

ER -