Solar Radiation Forecasting Using Machine Learning and Ensemble Feature Selection

Edna S. Solano (), Payman Dehghanian and Carolina M. Affonso
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Edna S. Solano: Faculty of Electrical Engineering, Federal University of Para, Belem 66075-110, PA, Brazil
Payman Dehghanian: Department of Electrical and Computer Engineering, The George Washington University, Washington, DC 20052, USA
Carolina M. Affonso: Faculty of Electrical Engineering, Federal University of Para, Belem 66075-110, PA, Brazil

Energies, 2022, vol. 15, issue 19, 1-18

Abstract: Accurate solar radiation forecasting is essential to operate power systems safely under high shares of photovoltaic generation. This paper compares the performance of several machine learning algorithms for solar radiation forecasting using endogenous and exogenous inputs and proposes an ensemble feature selection method to choose not only the most related input parameters but also their past observations values. The machine learning algorithms used are: Support Vector Regression (SVR), Extreme Gradient Boosting (XGBT), Categorical Boosting (CatBoost) and Voting-Average (VOA), which integrates SVR, XGBT and CatBoost. The proposed ensemble feature selection is based on Pearson coefficient, random forest, mutual information and relief. Prediction accuracy is evaluated based on several metrics using a real database from Salvador, Brazil. Different prediction time-horizons are considered: 1 h, 2 h and 3 h ahead. Numerical results demonstrate that the proposed ensemble feature selection approach improves forecasting accuracy and that VOA performs better than the other algorithms in all prediction time horizons.

Keywords: ensemble feature selection; machine learning; photovoltaic generation; solar radiation forecasting (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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