An unsupervised learning approach for predicting wind farm power and downstream wakes using weather patterns

2023/02/12 by Mariana Clare, Simon C. Warder, Clare, Mariana C A +7 · 1 citation
Engineering · Social Sciences · #62H30 #Atmospheric and Oceanic Physics (physics.ao-ph) #Energy Load and Power Forecasting #FOS: Computer and information sciences #FOS: Physical sciences #I.5.3 #I.5.4 #J.2 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Social Acceptance of Renewable Energy #Wind Energy Research and Development

paper · pdf · doi:10.48550/arxiv.2302.05886

openalex publication_date 2023/02/12 · openalex created_date 2023/02/16 · openalex updated_date 2026/07/28

Abstract

This repository contains the code to accompany the following paper: Mariana C. A. Clare, Simon C. Warder, Robert Neal, B. Bhaskaran, Matthew D. Piggott An unsupervised learning approach for predicting wind farm power and downstream wakes using weather patterns The paper combines the weather patterns found using k-means clustering of ERA5 data with the accurate numerical model WRF to determine both accurate long-term wind farm power estimates and long-term predictions of power loss due to wakes from upstream farms.

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