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For an electric company, accurate forecasts of electrical production and maintenance from wind farms are essential for efficient operation in markets like OMIE, OMIP, and MIBEL. Predictive models using Machine Learning and AI techniques like SVM or SVR, based on meteorological data and wind farm specifics, offer short-term accuracy but lack depth. A new research work proposes a Deep Neural Network (DNN) model for more precise long-term forecasts, considering wind farm characteristics and climatic data, aiming to outperform conventional forecasting methods with more detailed results.

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Wind Energy and Maintenance Forecasting Employing Tree-Based Reduction Methods

  • 会议论文

  • 发表日期：

    2025年

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  • Javier Sánchez-Soriano,
  • Pedro Jose Paniagua-Falo,
  • Carlos Quiterio Gómez Muñoz,
  • Alberto Pliego Marugán,
  • Jesús María Pinar-Pérez

摘要

For an electric company, accurate forecasts of electrical production and maintenance from wind farms are essential for efficient operation in markets like OMIE, OMIP, and MIBEL. Predictive models using Machine Learning and AI techniques like SVM or SVR, based on meteorological data and wind farm specifics, offer short-term accuracy but lack depth. A new research work proposes a Deep Neural Network (DNN) model for more precise long-term forecasts, considering wind farm characteristics and climatic data, aiming to outperform conventional forecasting methods with more detailed results.