Feature Selection by Binary Differential Evolution for Predicting the Energy Production of a Wind Plant

Sameer Al-Dahidi, Piero Baraldi (), Miriam Fresc, Enrico Zio and Lorenzo Montelatici
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Sameer Al-Dahidi: Department of Mechanical and Maintenance Engineering, School of Applied Technical Sciences, German Jordanian University, Amman 11180, Jordan
Piero Baraldi: Energy Department, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy
Miriam Fresc: Energy Department, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy
Enrico Zio: Energy Department, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy
Lorenzo Montelatici: Research Development and Innovation, Edison Spa, 20121 Milan, Italy

Energies, 2024, vol. 17, issue 10, 1-19

Abstract: We propose a method for selecting the optimal set of weather features for wind energy prediction. This problem is tackled by developing a wrapper approach that employs binary differential evolution to search for the best feature subset, and an ensemble of artificial neural networks to predict the energy production from a wind plant. The main novelties of the approach are the use of features provided by different weather forecast providers and the use of an ensemble composed of a reduced number of models for the wrapper search. Its effectiveness is verified using weather and energy production data collected from a 34 MW real wind plant. The model is built using the selected optimal subset of weather features and allows for (i) a 1% reduction in the mean absolute error compared with a model that considers all available features and a 4.4% reduction compared with the model currently employed by the plant owners, and (ii) a reduction in the number of selected features by 85% and 50%, respectively. Reducing the number of features boosts the prediction accuracy. The implication of this finding is significant as it allows plant owners to create profitable offers in the energy market and efficiently manage their power unit commitment, maintenance scheduling, and energy storage optimization.

Keywords: wind energy; prediction; feature selection; binary differential evolution; artificial neural networks; ensemble (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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