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A Hybrid Online Forecasting Model for Ultrashort-Term Photovoltaic Power Generation

Author

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  • Fei Mei

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
    Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Southeast University, Nanjing 210096, China)

  • Yi Pan

    (Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Southeast University, Nanjing 210096, China
    School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Kedong Zhu

    (Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Southeast University, Nanjing 210096, China
    School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Jianyong Zheng

    (Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Southeast University, Nanjing 210096, China
    School of Electrical Engineering, Southeast University, Nanjing 210096, China)

Abstract

A hybrid photovoltaic (PV) forecasting model is proposed for the ultrashort-term prediction of PV output. The model contains two parts: offline modeling and online forecasting. The offline module uses historical monitoring data to establish a weather type classification model and PV output regression submodels. The online module uses real-time monitoring data for weather type identification on target days and the forecasting of irradiation intensity and temperature time series. The appropriate regression submodel can be selected based on the subsequent results, and the ultrashort-term real-time forecasting of PV output can be performed over a short time scale. The model incorporates power generation and historical meteorological data from the PV station and is suitable for practical engineering applications. In addition to the irradiation intensity and temperature, other factors related to photovoltaic output are evaluated; however, they are excluded from the model for simplicity and efficiency. The performance of the model is verified by practical modeling analysis.

Suggested Citation

  • Fei Mei & Yi Pan & Kedong Zhu & Jianyong Zheng, 2018. "A Hybrid Online Forecasting Model for Ultrashort-Term Photovoltaic Power Generation," Sustainability, MDPI, vol. 10(3), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:3:p:820-:d:136440
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    References listed on IDEAS

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    Cited by:

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    3. Li, Qing & Zhang, Xinyan & Ma, Tianjiao & Jiao, Chunlei & Wang, Heng & Hu, Wei, 2021. "A multi-step ahead photovoltaic power prediction model based on similar day, enhanced colliding bodies optimization, variational mode decomposition, and deep extreme learning machine," Energy, Elsevier, vol. 224(C).
    4. Ai, Chunyu & He, Shan & Fan, Xiaochao & Wang, Weiqing, 2023. "Chaotic time series wind power prediction method based on OVMD-PE and improved multi-objective state transition algorithm," Energy, Elsevier, vol. 278(C).
    5. Chen, Xiang & Ding, Kun & Zhang, Jingwei & Han, Wei & Liu, Yongjie & Yang, Zenan & Weng, Shuai, 2022. "Online prediction of ultra-short-term photovoltaic power using chaotic characteristic analysis, improved PSO and KELM," Energy, Elsevier, vol. 248(C).
    6. Wei Li & Hui Ren & Ping Chen & Yanyang Wang & Hailong Qi, 2020. "Key Operational Issues on the Integration of Large-Scale Solar Power Generation—A Literature Review," Energies, MDPI, vol. 13(22), pages 1-25, November.
    7. Orest Lozynskyy & Damian Mazur & Yaroslav Marushchak & Bogdan Kwiatkowski & Andriy Lozynskyy & Tadeusz Kwater & Bohdan Kopchak & Przemysław Hawro & Lidiia Kasha & Robert Pękala & Robert Ziemba & Bogus, 2021. "Formation of Characteristic Polynomials on the Basis of Fractional Powers j of Dynamic Systems and Stability Problems of Such Systems," Energies, MDPI, vol. 14(21), pages 1-35, November.

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