Offshore Wind Energy Prediction Using Machine Learning with Multi-Resolution Inputs

Feng Ye (), Travis Miles () and Ahmed Aziz Ezzat ()
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Feng Ye: The State University of NJ
Travis Miles: The State University of NJ
Ahmed Aziz Ezzat: The State University of NJ

A chapter in Multimodal and Tensor Data Analytics for Industrial Systems Improvement, 2024, pp 167-183 from Springer

Abstract: Abstract The ever-increasing scale and penetration of offshore wind energy in modern day electricity systems is continually raising the need for wind resource and generation forecasts that are of higher quality and finer resolution, both spatially and temporally. In their quest for high-quality forecasts, a forecaster is often faced with the challenge of how to effectively make use of the wealth of heterogeneous data inputs at their disposal (e.g., on-site and off-site sensory data, multi-resolution weather predictions, and physics-based information), each characterized by different levels of accuracy, resolution, and/or fidelity. If utilized wisely, such multi-source information can collectively provide the forecaster with complementary “world views” of the local wind conditions at the wind farm site, ultimately enhancing the effectiveness of their forecasting approach. Machine learning (ML) presents a powerful approach to undertake this complex data fusion task, yet valid concerns about its “black-box-ness” and indifference to the underlying physics of wind field formation and propagation are often raised. This chapter briefly reviews the main lines of research on this front and then presents an ML-based approach to effectively integrate multi-resolution physics-based and data-driven information in order to make accurate short-term wind speed and power forecasts for the offshore wind energy areas in the US NY/NJ Bight where several Gigawatt-scale wind farms are under development.

Keywords: Physics guided machine learning; Multi-resolution inputs; Offshore wind energy forecasting (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-53092-0_8

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