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Using Crested Porcupine Optimizer Algorithm and CNN-LSTM-Attention Model Combined with Deep Learning Methods to Enhance Short-Term Power Forecasting in PV Generation

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  • Yiling Fan

    (Hebei Key Laboratory of Intelligent Data Information Processing and Control, Tangshan University, Tangshan 063000, China
    Tangshan Key Laboratory of Intelligent Motion Control System, Tangshan University, Tangshan 063000, China)

  • Zhuang Ma

    (Hebei Key Laboratory of Intelligent Data Information Processing and Control, Tangshan University, Tangshan 063000, China
    Tangshan Key Laboratory of Intelligent Motion Control System, Tangshan University, Tangshan 063000, China)

  • Wanwei Tang

    (Hebei Key Laboratory of Intelligent Data Information Processing and Control, Tangshan University, Tangshan 063000, China
    Tangshan Key Laboratory of Intelligent Motion Control System, Tangshan University, Tangshan 063000, China)

  • Jing Liang

    (Hebei Key Laboratory of Intelligent Data Information Processing and Control, Tangshan University, Tangshan 063000, China
    Tangshan Key Laboratory of Intelligent Motion Control System, Tangshan University, Tangshan 063000, China)

  • Pengfei Xu

    (Hebei Key Laboratory of Intelligent Data Information Processing and Control, Tangshan University, Tangshan 063000, China
    Tangshan Key Laboratory of Intelligent Motion Control System, Tangshan University, Tangshan 063000, China)

Abstract

Due to the inherent intermittency, variability, and randomness, photovoltaic (PV) power generation faces significant challenges in energy grid integration. To address these challenges, current research mainly focuses on developing more efficient energy management systems and prediction technologies. Through optimizing scheduling and integration in PV power generation, the stability and reliability of the power grid can be further improved. In this study, a new prediction model is introduced that combines the strengths of convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and attention mechanisms, so we call this algorithm CNN-LSTM-Attention (CLA). In addition, the Crested Porcupine Optimizer (CPO) algorithm is utilized to solve the short-term prediction problem in photovoltaic power generation. This model is abbreviated as CPO-CLA. This is the first time that the CPO algorithm has been introduced into the LSTM algorithm for parameter optimization. To effectively capture univariate and multivariate time series patterns, multiple relevant and target variables prediction patterns (MRTPPs) are employed in the CPO-CLA model. The results show that the CPO-CLA model is superior to traditional methods and recent popular models in terms of prediction accuracy and stability, especially in the 13 h timestep. The integration of attention mechanisms enables the model to adaptively focus on the most relevant historical data for future power prediction. The CPO algorithm further optimizes the LSTM network parameters, which ensures the robust generalization ability of the model. The research results are of great significance for energy generation scheduling and establishing trust in the energy market. Ultimately, it will help integrate renewable energy into the grid more reliably and efficiently.

Suggested Citation

  • Yiling Fan & Zhuang Ma & Wanwei Tang & Jing Liang & Pengfei Xu, 2024. "Using Crested Porcupine Optimizer Algorithm and CNN-LSTM-Attention Model Combined with Deep Learning Methods to Enhance Short-Term Power Forecasting in PV Generation," Energies, MDPI, vol. 17(14), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:14:p:3435-:d:1433827
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    References listed on IDEAS

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    1. Kumari, Pratima & Toshniwal, Durga, 2021. "Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting," Applied Energy, Elsevier, vol. 295(C).
    2. Elizabeth Michael, Neethu & Hasan, Shazia & Al-Durra, Ahmed & Mishra, Manohar, 2022. "Short-term solar irradiance forecasting based on a novel Bayesian optimized deep Long Short-Term Memory neural network," Applied Energy, Elsevier, vol. 324(C).
    3. Hu, Zehuan & Gao, Yuan & Ji, Siyu & Mae, Masayuki & Imaizumi, Taiji, 2024. "Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data," Applied Energy, Elsevier, vol. 359(C).
    4. Altaf Hussain & Zulfiqar Ahmad Khan & Tanveer Hussain & Fath U Min Ullah & Seungmin Rho & Sung Wook Baik & Chun Wei, 2022. "A Hybrid Deep Learning-Based Network for Photovoltaic Power Forecasting," Complexity, Hindawi, vol. 2022, pages 1-12, October.
    5. Yan, Qin & Lu, Zhiying & Liu, Hong & He, Xingtang & Zhang, Xihai & Guo, Jianlin, 2024. "Short-term prediction of integrated energy load aggregation using a bi-directional simple recurrent unit network with feature-temporal attention mechanism ensemble learning model," Applied Energy, Elsevier, vol. 355(C).
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