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Wind Energy Prediction Using Machine Learning

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Document pages: 21 pages

Abstract: Wind energy prediction represents an important and active field in therenewable energy sector. Since renewable energy sources are integrated intoexisting grids and combined with traditional sources, knowing the amount ofenergy that will be produced is key in minimizing the operational cost of thewind farm and safe operation of the power grid. In this context, we propose acomparative and comprehensive study of artificial neural networks, supportvector regression, random trees, and random forest, and present the pros andcons of implementing the aforementioned techniques. A step-by-step approachbased on the CRISP-DM data mining framework reveals the thought processend-to-end, including feature engineering, metrics selection, model selection,or hyperparameter tuning. Using the selectedmetrics for model evaluation, we provide a summary highlighting the optimalresults and the trade-off between performance and the resources expended toachieve these results. This research is also intended to provide guidance for windenergy professionals, filling the gap between purely academic research andreal-world business use cases, providing the exact architectures andselected hyperparameters.

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