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A Convolutional Neural Network–Long Short-Term Memory–Attention Solar Photovoltaic Power Prediction–Correction Model Based on the Division of Twenty-Four Solar Terms

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  • Guodong Wu

    (College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
    Power Dispatch Center of State Grid Gansu Electric Power Company, Lanzhou 730030, China)

  • Diangang Hu

    (Power Dispatch Center of State Grid Gansu Electric Power Company, Lanzhou 730030, China)

  • Yongrui Zhang

    (Power Dispatch Center of State Grid Gansu Electric Power Company, Lanzhou 730030, China)

  • Guangqing Bao

    (School of Electronics and Information Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Ting He

    (Gansu Natural Energy Research Institute, Lanzhou 730046, China)

Abstract

The prevalence of extreme weather events gives rise to a significant degree of prediction bias in the forecasting of photovoltaic (PV) power. In order to enhance the precision of forecasting outcomes, this study examines the interrelationships between China’s 24 conventional solar terms and extreme meteorological events. Additionally, it proposes a methodology for estimating the short-term generation of PV power based on the division of solar term time series. Firstly, given that the meteorological data from the same festival is more representative of the climate state at the current prediction moment, the sample data are grouped according to the 24 festival time nodes. Secondly, a convolutional neural network–long short-term memory (CNN-LSTM) PV power prediction model based on an Attention mechanism is proposed. This model extracts temporal change information from nonlinear sample data through LSTM, and a CNN link is added at the front end of LSTM to address the issue of LSTM being unable to obtain the spatial linkage of multiple features. Additionally, an Attention mechanism is incorporated at the back end of the CNN to obtain the feature information of crucial time steps, further reducing the multi-step prediction error. Concurrently, a PV power error prediction model is constructed to rectify the outcomes of the aforementioned prediction model. The examination of the measured data from PV power stations and the comparison and analysis with other prediction models demonstrate that the model presented in this paper can effectively enhance the accuracy of PV power predictions.

Suggested Citation

  • Guodong Wu & Diangang Hu & Yongrui Zhang & Guangqing Bao & Ting He, 2024. "A Convolutional Neural Network–Long Short-Term Memory–Attention Solar Photovoltaic Power Prediction–Correction Model Based on the Division of Twenty-Four Solar Terms," Energies, MDPI, vol. 17(22), pages 1-19, November.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5549-:d:1515429
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    References listed on IDEAS

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    1. Xianping Zhu & Shaowu Li & Jingxun Fan, 2023. "An Overall Linearized Modeling Method and Associated Delay Time Model for the PV System," Energies, MDPI, vol. 16(10), pages 1-37, May.
    2. Xiaoying Ren & Fei Zhang & Junshuai Yan & Yongqian Liu, 2024. "A Novel Convolutional Neural Net Architecture Based on Incorporating Meteorological Variable Inputs into Ultra-Short-Term Photovoltaic Power Forecasting," Sustainability, MDPI, vol. 16(7), pages 1-21, March.
    3. Zhang, Chu & Tao, Zihan & Xiong, Jinlin & Qian, Shijie & Fu, Yongyan & Ji, Jie & Nazir, Muhammad Shahzad & Peng, Tian, 2024. "Research and application of a novel weight-based evolutionary ensemble model using principal component analysis for wind power prediction," Renewable Energy, Elsevier, vol. 232(C).
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