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Solar Irradiation Forecasting Using Ensemble Voting Based on Machine Learning Algorithms

Author

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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)

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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    1. Tomás Gavilánez & Néstor Zamora & Josué Navarrete & Nino Vega & Gabriela Vergara, 2025. "AI-Based Virtual Assistant for Solar Radiation Prediction and Improvement of Sustainable Energy Systems," Sustainability, MDPI, vol. 17(19), pages 1-20, October.
    2. Francisco A. Ramírez-Rivera & Néstor F. Guerrero-Rodríguez, 2024. "Ensemble Learning Algorithms for Solar Radiation Prediction in Santo Domingo: Measurements and Evaluation," Sustainability, MDPI, vol. 16(18), pages 1-27, September.
    3. Chibuike Chiedozie Ibebuchi, 2025. "Day-Ahead Energy Price Forecasting with Machine Learning: Role of Endogenous Predictors," Forecasting, MDPI, vol. 7(2), pages 1-16, April.
    4. Latif Bukari Rashid & Shahzada Zaman Shuja & Shafiqur Rehman, 2025. "Machine Learning Forecasting of Direct Solar Radiation: A Multi-Model Evaluation with Trigonometric Cyclical Encoding," Forecasting, MDPI, vol. 7(4), pages 1-25, October.
    5. Thiago Conte & Roberto Oliveira, 2024. "Comparative Analysis between Intelligent Machine Committees and Hybrid Deep Learning with Genetic Algorithms in Energy Sector Forecasting: A Case Study on Electricity Price and Wind Speed in the Brazi," Energies, MDPI, vol. 17(4), pages 1-31, February.

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