Solar Irradiation Forecasting Using Ensemble Voting Based on Machine Learning Algorithms

Edna S. Solano () and Carolina M. Affonso
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Edna S. Solano: Faculty of Electrical Engineering, Federal University of Para, Belem 66075-110, PA, Brazil
Carolina M. Affonso: Faculty of Electrical Engineering, Federal University of Para, Belem 66075-110, PA, Brazil

Sustainability, 2023, vol. 15, issue 10, 1-19

Abstract: This paper proposes an ensemble voting model for solar radiation forecasting based on machine learning algorithms. Several ensemble models are assessed using a simple average and a weighted average, combining the following algorithms: random forest, extreme gradient boosting, categorical boosting, and adaptive boosting. A clustering algorithm is used to group data according to the weather, and feature selection is applied to choose the most-related inputs and their past observation values. Prediction performance is evaluated by several metrics using a real-world Brazilian database, considering different prediction time horizons of up to 12 h ahead. Numerical results show the weighted average voting approach based on random forest and categorical boosting has superior performance, with an average reduction of 6% for MAE, 3% for RMSE, 16% for MAPE, and 1% for R 2 when predicting one hour in advance, outperforming individual machine learning algorithms and other ensemble models.

Keywords: clustering; ensemble voting; feature selection; machine learning; solar irradiation forecasting (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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