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Photovoltaic Power Forecasting Based on EEMD and a Variable-Weight Combination Forecasting Model

Author

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  • Hui Wang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Jianbo Sun

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Weijun Wang

    (Department of Economics and Management, North China Electric Power University, Baoding 071003, Hebei, China)

Abstract

It is widely considered that solar energy will be one of the most competitive energy sources in the future, and solar energy currently accounts for high percentages of power generation in developed countries. However, its power generation capacity is significantly affected by several factors; therefore, accurate prediction of solar power generation is necessary. This paper proposes a photovoltaic (PV) power generation forecasting method based on ensemble empirical mode decomposition (EEMD) and variable-weight combination forecasting. First, EEMD is applied to decompose PV power data into components that are then combined into three groups: low-frequency, intermediate-frequency, and high-frequency. These three groups of sequences are individually predicted by the variable-weight combination forecasting model and added to obtain the final forecasting result. In addition, the design of the weights for combination forecasting was studied during the forecasting process. The comparison in the case study indicates that in PV power generation forecasting, the prediction results obtained by the individual forecasting and summing of the sequences after the EEMD are better than those from direct prediction. In addition, when the single prediction model is converted to a variable-weight combination forecasting model, the prediction accuracy is further improved by using the optimal weights.

Suggested Citation

  • Hui Wang & Jianbo Sun & Weijun Wang, 2018. "Photovoltaic Power Forecasting Based on EEMD and a Variable-Weight Combination Forecasting Model," Sustainability, MDPI, vol. 10(8), pages 1-11, July.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:8:p:2627-:d:160111
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    Cited by:

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    2. Hongbo Gao & Shuang Qiu & Jun Fang & Nan Ma & Jiye Wang & Kun Cheng & Hui Wang & Yidong Zhu & Dawei Hu & Hengyu Liu & Jun Wang, 2023. "Short-Term Prediction of PV Power Based on Combined Modal Decomposition and NARX-LSTM-LightGBM," Sustainability, MDPI, vol. 15(10), pages 1-22, May.
    3. Yuquan Meng & Yuhang Yang & Haseung Chung & Pil-Ho Lee & Chenhui Shao, 2018. "Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review," Sustainability, MDPI, vol. 10(12), pages 1-28, December.
    4. Wei Ma & Wei Wang & Xuezhi Wu & Ruonan Hu & Fen Tang & Weige Zhang, 2019. "Control Strategy of a Hybrid Energy Storage System to Smooth Photovoltaic Power Fluctuations Considering Photovoltaic Output Power Curtailment," Sustainability, MDPI, vol. 11(5), pages 1-22, March.
    5. Xiong, Jinlin & Peng, Tian & Tao, Zihan & Zhang, Chu & Song, Shihao & Nazir, Muhammad Shahzad, 2023. "A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction," Energy, Elsevier, vol. 266(C).
    6. Li, Fengyun & Zheng, Haofeng & Li, Xingmei, 2022. "A novel hybrid model for multi-step ahead photovoltaic power prediction based on conditional time series generative adversarial networks," Renewable Energy, Elsevier, vol. 199(C), pages 560-586.
    7. Hossein Moayedi & Amir Mosavi, 2021. "An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework," Energies, MDPI, vol. 14(4), pages 1-18, February.

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