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Global horizontal radiation forecast using forward regression on a quadratic kernel support vector machine: Case study of the Tibet Autonomous Region in China

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  • Jiang, He
  • Dong, Yao

Abstract

Effective and accurate forecasting of solar radiation plays a critically important role in the design of grid-connected photovoltaic installations. However, this is an extremely challenging task because of inconsistencies in variable selection and the prohibitively expensive computational cost as the number of variables increases. Although the support vector machine (SVM) can be applied to forecast solar radiation, it includes a large number of redundant variables. With the intent of establishing an interpretable model, a penalized SVM has been proposed. However, these penalized approaches shrink the estimate, which results in inaccurate results. In order to overcome these drawbacks and improve the accuracy of forecasting, this study develops a novel approach referred to as “forward regression on the quadratic kernel support vector machine” (QKSVM-FR) for building a quadratic regression model using forward regression to select the important variables for forecasting the global horizontal radiation in the Tibet Autonomous Region. A fast and simple-to-implement computational algorithm is derived to perform the variable selection and forecasting tasks simultaneously. Furthermore, the SVM information criterion is utilized to select the kernel parameter to guarantee model consistency. The results of experiments directly confirm the outstanding forecasting performance of the proposed QKSVM-FR method compared to other existing methods.

Suggested Citation

  • Jiang, He & Dong, Yao, 2017. "Global horizontal radiation forecast using forward regression on a quadratic kernel support vector machine: Case study of the Tibet Autonomous Region in China," Energy, Elsevier, vol. 133(C), pages 270-283.
  • Handle: RePEc:eee:energy:v:133:y:2017:i:c:p:270-283
    DOI: 10.1016/j.energy.2017.05.124
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    Citations

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    Cited by:

    1. Hanany Tolba & Nouha Dkhili & Julien Nou & Julien Eynard & Stéphane Thil & Stéphane Grieu, 2020. "Multi-Horizon Forecasting of Global Horizontal Irradiance Using Online Gaussian Process Regression: A Kernel Study," Energies, MDPI, vol. 13(16), pages 1-23, August.
    2. Xu, Lei & Hou, Lei & Zhu, Zhenyu & Li, Yu & Liu, Jiaquan & Lei, Ting & Wu, Xingguang, 2021. "Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm," Energy, Elsevier, vol. 222(C).
    3. Niu, Tong & Li, Jinkai & Wei, Wei & Yue, Hui, 2022. "A hybrid deep learning framework integrating feature selection and transfer learning for multi-step global horizontal irradiation forecasting," Applied Energy, Elsevier, vol. 326(C).
    4. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2022. "Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms," Applied Energy, Elsevier, vol. 316(C).
    5. Wang, Zheng-Xin & Wang, Zhi-Wei & Li, Qin, 2020. "Forecasting the industrial solar energy consumption using a novel seasonal GM(1,1) model with dynamic seasonal adjustment factors," Energy, Elsevier, vol. 200(C).
    6. He Jiang, 2023. "Forecasting global solar radiation using a robust regularization approach with mixture kernels," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 1989-2010, December.
    7. Rodríguez, Fermín & Martín, Fernando & Fontán, Luis & Galarza, Ainhoa, 2021. "Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power," Energy, Elsevier, vol. 229(C).
    8. Wang, Jianzhou & Yang, Wendong & Du, Pei & Li, Yifan, 2018. "Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system," Energy, Elsevier, vol. 148(C), pages 59-78.
    9. Peng, Tian & Zhang, Chu & Zhou, Jianzhong & Nazir, Muhammad Shahzad, 2021. "An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting," Energy, Elsevier, vol. 221(C).
    10. Fouilloy, Alexis & Voyant, Cyril & Notton, Gilles & Motte, Fabrice & Paoli, Christophe & Nivet, Marie-Laure & Guillot, Emmanuel & Duchaud, Jean-Laurent, 2018. "Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability," Energy, Elsevier, vol. 165(PA), pages 620-629.
    11. Amber, K.P. & Ahmad, R. & Aslam, M.W. & Kousar, A. & Usman, M. & Khan, M.S., 2018. "Intelligent techniques for forecasting electricity consumption of buildings," Energy, Elsevier, vol. 157(C), pages 886-893.

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