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Short-term PV power forecast methodology based on multi-scale fluctuation characteristics extraction

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  • Zhu, Jiebei
  • Li, Mingrui
  • Luo, Lin
  • Zhang, Bidan
  • Cui, Mingjian
  • Yu, Lujie

Abstract

The short-term forecast of photovoltaic (PV) power is crucial for the security and economics of power system operations. However, the fluctuation characteristics of the PV power, which are closely related to the meteorological factors, introduce inaccuracies in its forecast. Towards this end, the paper studies the effects of clustering analysis at long time scale and data reconstruction technique at short time scale on capturing PV power fluctuation characteristics. A short-term PV power forecasts method based on multi-scale fluctuation characteristics extraction (MFCE), which employs a path analysis to identify the relevance of meteorological factors with PV power at long time scale and a phase space reconstruction to analyze PV power fluctuation characteristics at short time scale, is proposed in this paper. The proposed MFCE methodology deploys a widely-used extreme gradient boosting (XGBoost) model to output the forecasting results. Both the effectiveness and accuracy of the proposed methodology are verified by using the real data under the conditions of sunny and cloudy days of four seasons compared to traditional methodologies.

Suggested Citation

  • Zhu, Jiebei & Li, Mingrui & Luo, Lin & Zhang, Bidan & Cui, Mingjian & Yu, Lujie, 2023. "Short-term PV power forecast methodology based on multi-scale fluctuation characteristics extraction," Renewable Energy, Elsevier, vol. 208(C), pages 141-151.
    • Handle: RePEc:eee:renene:v:208:y:2023:i:c:p:141-151
    DOI: 10.1016/j.renene.2023.03.029
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    6. Sun, Qihui & Yan, Feng & Sun, Wanqing & Zhou, Yuqing, 2025. "DWT-Former: Fusing wavelet-based multi-scale features and transformer-based temporal representations for photovoltaic power forecasting," Energy, Elsevier, vol. 341(C).
    7. Chuang Yin & Nan Wei & Jinghang Wu & Chuhong Ruan & Xi Luo & Fanhua Zeng, 2024. "An Empirical Mode Decomposition-Based Hybrid Model for Sub-Hourly Load Forecasting," Energies, MDPI, vol. 17(2), pages 1-17, January.
    8. Yang, Mao & Jiang, Yue & Zhang, Wei & Li, Yi & Su, Xin, 2024. "Short-term interval prediction strategy of photovoltaic power based on meteorological reconstruction with spatiotemporal correlation and multi-factor interval constraints," Renewable Energy, Elsevier, vol. 237(PC).
    9. Fang, Mingyu & Qian, Weixing & Qian, Tao & Bao, Qiwei & Zhang, Haocheng & Qiu, Xiao, 2024. "DGImNet: A deep learning model for photovoltaic soiling loss estimation," Applied Energy, Elsevier, vol. 376(PB).
    10. Gong, Jianqiang & Qu, Zhiguo & Zhu, Zhenle & Xu, Hongtao, 2025. "Parallel TimesNet-BiLSTM model for ultra-short-term photovoltaic power forecasting using STL decomposition and auto-tuning," Energy, Elsevier, vol. 320(C).
    11. Wu, Zhiyuan & Fang, Guohua & Ye, Jian & Zhu, David Z. & Huang, Xianfeng, 2025. "A reinforcement learning-based ensemble forecasting framework for renewable energy forecasting," Renewable Energy, Elsevier, vol. 244(C).
    12. Sun, Fengpeng & Li, Longhao & Bian, Dunxin & Bian, Wenlin & Wang, Qinghong & Wang, Shuang, 2025. "Photovoltaic power prediction based on multi-scale photovoltaic power fluctuation characteristics and multi-channel LSTM prediction models," Renewable Energy, Elsevier, vol. 246(C).
    13. Ridha, Hussein Mohammed & Ahmadipour, Masoud & Alghrairi, Mokhalad & Hizam, Hashim & Mirjalili, Seyedali & Zubaidi, Salah L. & Mohammed S, Marwa Y., 2026. "A novel hybrid photovoltaic current prediction model utilizing singular spectrum analysis, adaptive beluga whale optimization, and improved extreme learning machine," Renewable Energy, Elsevier, vol. 256(PA).
    14. Chen, Rujian & Liu, Gang & Cao, Yisheng & Xiao, Gang & Tang, Jianchao, 2024. "CGAformer: Multi-scale feature Transformer with MLP architecture for short-term photovoltaic power forecasting," Energy, Elsevier, vol. 312(C).
    15. Zhang, Ruoyang & Wu, Yu & Zhang, Lei & Xu, Chongbin & Wang, ZeYu & Zhang, Yanfeng & Sun, Xiaomin & Zuo, Xin & Wu, Yuhan & Chen, Qian, 2025. "A multiscale network with mixed features and extended regional weather forecasts for predicting short-term photovoltaic power," Energy, Elsevier, vol. 318(C).
    16. Mingyi Liu & Bin Zhang & Jiaqi Wang & Han Liu & Jianxing Wang & Chenghao Liu & Jiahui Zhao & Yue Sun & Rongrong Zhai & Yong Zhu, 2023. "Optimal Configuration of Wind-PV and Energy Storage in Large Clean Energy Bases," Sustainability, MDPI, vol. 15(17), pages 1-23, August.

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