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Stacking Model for Photovoltaic-Power-Generation Prediction

Author

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  • Hongchao Zhang

    (School of Business, Sun Yat-sen University, Guangzhou 510006, China
    Hongchao Zhang and Tengteng Zhu contributed equally, and the order of authorship is alphabetically determined.)

  • Tengteng Zhu

    (International Innovation Research Center, Guangdong University of Foreign Studies, Guangzhou 510006, China
    Hongchao Zhang and Tengteng Zhu contributed equally, and the order of authorship is alphabetically determined.)

Abstract

Despite the clean and renewable advantages of solar energy, the instability of photovoltaic power generation limits its wide applicability. In order to ensure stable power-grid operations and the safe dispatching of the power grid, it is necessary to develop a model that can accurately predict the photovoltaic power generation. As a widely used prediction method, the stacking model has been applied in many fields. However, few studies have used stacking models to predict photovoltaic power generation. In the research, we develop four different stacking models that are based on extreme gradient boosting, random forest, light gradient boosting, and gradient boosting decision tree to predict photovoltaic power generation, by using two datasets. The results show that the prediction accuracy of the stacking model is higher than that of the single ensemble-learning model, and that the prediction accuracy of the Stacking-GBDT model is higher than the other stacking models. The stacking model that is proposed in this research provides a reference for the accurate prediction of photovoltaic power generation.

Suggested Citation

  • Hongchao Zhang & Tengteng Zhu, 2022. "Stacking Model for Photovoltaic-Power-Generation Prediction," Sustainability, MDPI, vol. 14(9), pages 1-16, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5669-:d:810783
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    References listed on IDEAS

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    3. Abdallah Abdellatif & Hamza Mubarak & Shameem Ahmad & Tofael Ahmed & G. M. Shafiullah & Ahmad Hammoudeh & Hamdan Abdellatef & M. M. Rahman & Hassan Muwafaq Gheni, 2022. "Forecasting Photovoltaic Power Generation with a Stacking Ensemble Model," Sustainability, MDPI, vol. 14(17), pages 1-21, September.
    4. Hui Huang & Qiliang Zhu & Xueling Zhu & Jinhua Zhang, 2023. "An Adaptive, Data-Driven Stacking Ensemble Learning Framework for the Short-Term Forecasting of Renewable Energy Generation," Energies, MDPI, vol. 16(4), pages 1-20, February.
    5. Jose Cruz & Christian Romero & Oscar Vera & Saul Huaquipaco & Norman Beltran & Wilson Mamani, 2023. "Multiparameter Regression of a Photovoltaic System by Applying Hybrid Methods with Variable Selection and Stacking Ensembles under Extreme Conditions of Altitudes Higher than 3800 Meters above Sea Lev," Energies, MDPI, vol. 16(12), pages 1-21, June.

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