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Ultrashort term photovoltaic power combinatorial forecasting model based on similar day clustering
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Qingsong CHANG1, Zhao YANG2, Yihun YANG2, Yang LEI2, Xinlin HE2
Thermal Power Generation | 2023, 52(11) : 123 - 131
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Thermal Power Generation | 2023, 52(11): 123-131
• Special topic on new energy power generation technology •
Ultrashort term photovoltaic power combinatorial forecasting model based on similar day clustering
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Qingsong CHANG1, Zhao YANG2, Yihun YANG2, Yang LEI2, Xinlin HE2
Affiliations
  • 1.Jiutai Power Plant, Huaneng Changchun Power Generation Co., Ltd., Changchun 130500, China
  • 2.Xi’an Thermal Power Research Institute Co., Ltd., Xi’an 710054, China
Published: 2023-11-25 doi: 10.19666/j.rlfd.202301023
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Aiming at the problem of low prediction accuracy of single power prediction model due to the impact of photovoltaic power fluctuation, a combined photovoltaic power prediction model based on similar day clustering is proposed. Firstly, k-means clustering is selected to divide the original power data into three similar day sample sets of sunny, rainy and cloudy according to different weather types, and the variational mode decomposition (VMD) is used to decompose the similar day samples; Secondly, the convolution neural network is used to optimize the support vector machine (CNN-SVM) and bidirectional short-term and short-term memory (BiLSTM) neural network, respectively, to predict and superimpose the decomposed power data and combine the prediction results with weights, and the grid search algorithm (GS) is used to find the optimal combination weight to improve the performance of the combination prediction model. Finally, the validity of the PV power prediction model proposed in this paper is verified by the one-year measured data of a photovoltaic power station in Australia. The experimental results show that the model proposed in this paper can predict the photovoltaic power well and has strong adaptability no matter what weather type.

PV power prediction  /  CNN  /  SVM  /  LSTM neural network  /  grid search algorithm
Qingsong CHANG, Zhao YANG, Yihun YANG, Yang LEI, Xinlin HE. Ultrashort term photovoltaic power combinatorial forecasting model based on similar day clustering[J]. Thermal Power Generation, 2023 , 52 (11) : 123 -131 . DOI: 10.19666/j.rlfd.202301023
Funding
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  • Science and Technology Project of China(HNKJ22-H36)
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Year 2023 volume 52 Issue 11
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Article Info
doi: 10.19666/j.rlfd.202301023
  • Receive Date：2023-01-11
  • Online Date：2026-01-26
  • Published：2023-11-25
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  • Received：2023-01-11
Funding
Science and Technology Project of China(HNKJ22-H36)
Affiliations
    1.Jiutai Power Plant, Huaneng Changchun Power Generation Co., Ltd., Changchun 130500, China
    2.Xi’an Thermal Power Research Institute Co., Ltd., Xi’an 710054, China
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https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202301023
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红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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