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A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion

Author

Listed:
  • Lei Fu

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Yiling Yang

    (Faculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, China)

  • Xiaolong Yao

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Xufen Jiao

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Tiantian Zhu

    (College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China)

Abstract

Photovoltaic (PV) power generation is greatly affected by meteorological environmental factors, with obvious fluctuations and intermittencies. The large-scale PV power generation grid connection has an impact on the source-load stability of the large power grid. To scientifically and rationally formulate the power dispatching plan, it is necessary to realize the PV output prediction. The output prediction of single power plants is no longer applicable to large-scale power dispatching. Therefore, the demand for the PV output prediction of multiple power plants in an entire region is becoming increasingly important. In view of the drawbacks of the traditional regional PV output prediction methods, which divide a region into sub-regions based on geographical locations and determine representative power plants according to the correlation coefficient, this paper proposes a multilevel spatial upscaling regional PV output prediction algorithm. Firstly, the sub-region division is realized by an empirical orthogonal function (EOF) decomposition and hierarchical clustering. Secondly, a representative power plant selection model is established based on the minimum redundancy maximum relevance (mRMR) criterion. Finally, the PV output prediction for the entire region is achieved through the output prediction of representative power plants of the sub-regions by utilizing the Elman neural network. The results from a case study show that, compared with traditional methods, the proposed prediction method reduces the normalized mean absolute error (nMAE) by 4.68% and the normalized root mean square error (nRMSE) by 5.65%, thereby effectively improving the prediction accuracy.

Suggested Citation

  • Lei Fu & Yiling Yang & Xiaolong Yao & Xufen Jiao & Tiantian Zhu, 2019. "A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion," Energies, MDPI, vol. 12(20), pages 1-23, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:20:p:3817-:d:274629
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    2. Dou, Weijing & Wang, Kai & Shan, Shuo & Li, Chenxi & Wang, Yiye & Zhang, Kanjian & Wei, Haikun & Sreeram, Victor, 2024. "Day-ahead Numerical Weather Prediction solar irradiance correction using a clustering method based on weather conditions," Applied Energy, Elsevier, vol. 365(C).
    3. Dengchang Ma & Rongyi Xie & Guobing Pan & Zongxu Zuo & Lidong Chu & Jing Ouyang, 2023. "Photovoltaic Power Output Prediction Based on TabNet for Regional Distributed Photovoltaic Stations Group," Energies, MDPI, vol. 16(15), pages 1-22, July.
    4. Taeyoung Kim & Jinho Kim, 2021. "A Regional Day-Ahead Rooftop Photovoltaic Generation Forecasting Model Considering Unauthorized Photovoltaic Installation," Energies, MDPI, vol. 14(14), pages 1-22, July.
    5. Ruifeng Shi & Penghui Zhang & Jie Zhang & Li Niu & Xiaoting Han, 2020. "Multidispatch for Microgrid including Renewable Energy and Electric Vehicles with Robust Optimization Algorithm," Energies, MDPI, vol. 13(11), pages 1-15, June.
    6. Perera, Maneesha & De Hoog, Julian & Bandara, Kasun & Senanayake, Damith & Halgamuge, Saman, 2024. "Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data," Applied Energy, Elsevier, vol. 361(C).

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