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Solar Irradiation Forecasting Using Ensemble Voting Based on Machine Learning Algorithms

Author

Listed:
  • 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)

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.

Suggested Citation

  • Edna S. Solano & Carolina M. Affonso, 2023. "Solar Irradiation Forecasting Using Ensemble Voting Based on Machine Learning Algorithms," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:7943-:d:1145564
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