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Solar Radiation Forecasting Using Machine Learning and Ensemble Feature Selection

Author

Listed:
  • 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)

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.

Suggested Citation

  • Edna S. Solano & Payman Dehghanian & Carolina M. Affonso, 2022. "Solar Radiation Forecasting Using Machine Learning and Ensemble Feature Selection," Energies, MDPI, vol. 15(19), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7049-:d:924895
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    References listed on IDEAS

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    1. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    2. Cai Tao & Junjie Lu & Jianxun Lang & Xiaosheng Peng & Kai Cheng & Shanxu Duan, 2021. "Short-Term Forecasting of Photovoltaic Power Generation Based on Feature Selection and Bias Compensation–LSTM Network," Energies, MDPI, vol. 14(11), pages 1-16, May.
    3. Dong, Zibo & Yang, Dazhi & Reindl, Thomas & Walsh, Wilfred M., 2015. "A novel hybrid approach based on self-organizing maps, support vector regression and particle swarm optimization to forecast solar irradiance," Energy, Elsevier, vol. 82(C), pages 570-577.
    4. Kumar, Rajesh & Aggarwal, R.K. & Sharma, J.D., 2015. "Comparison of regression and artificial neural network models for estimation of global solar radiations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1294-1299.
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    Cited by:

    1. Panagiotis Korkidis & Anastasios Dounis, 2023. "Intelligent Fuzzy Models: WM, ANFIS, and Patch Learning for the Competitive Forecasting of Environmental Variables," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
    2. Meysam Alizamir & Kaywan Othman Ahmed & Jalal Shiri & Ahmad Fakheri Fard & Sungwon Kim & Salim Heddam & Ozgur Kisi, 2023. "A New Insight for Daily Solar Radiation Prediction by Meteorological Data Using an Advanced Artificial Intelligence Algorithm: Deep Extreme Learning Machine Integrated with Variational Mode Decomposit," Sustainability, MDPI, vol. 15(14), pages 1-35, July.
    3. Jeehong Kim & Seok-ho Lee & Kil To Chong, 2022. "A Study of Neural Network Framework for Power Generation Prediction of a Solar Power Plant," Energies, MDPI, vol. 15(22), pages 1-19, November.
    4. Isaac Gallardo & Daniel Amor & Álvaro Gutiérrez, 2023. "Recent Trends in Real-Time Photovoltaic Prediction Systems," Energies, MDPI, vol. 16(15), pages 1-17, July.

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