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Optimization of Bi-LSTM Photovoltaic Power Prediction Based on Improved Snow Ablation Optimization Algorithm

Author

Listed:
  • Yuhan Wu

    (College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China)

  • Chun Xiang

    (School of Mechanical Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China)

  • Heng Qian

    (School of Mechanical Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China)

  • Peijian Zhou

    (College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China)

Abstract

To enhance the stability of photovoltaic power grid integration and improve power prediction accuracy, a photovoltaic power prediction method based on an improved snow ablation optimization algorithm (Good Point and Vibration Snow Ablation Optimizer, GVSAO) and Bi-directional Long Short-Term Memory (Bi-LSTM) network is proposed. Weather data is divided into three typical categories using K-means clustering, and data normalization is performed using the minmax method. The key structural parameters of Bi-LSTM, such as the feature dimension at each time step and the number of hidden units in each LSTM layer, are optimized based on the Good Point and Vibration strategy. A prediction model is constructed based on GVSAO-Bi-LSTM, and typical test functions are selected to analyze and evaluate the improved model. The research results show that the average absolute percentage error of the GVSAO-Bi-LSTM prediction model under sunny, cloudy, and rainy weather conditions are 4.75%, 5.41%, and 14.37%, respectively. Compared with other methods, the prediction results of this model are more accurate, verifying its effectiveness.

Suggested Citation

  • Yuhan Wu & Chun Xiang & Heng Qian & Peijian Zhou, 2024. "Optimization of Bi-LSTM Photovoltaic Power Prediction Based on Improved Snow Ablation Optimization Algorithm," Energies, MDPI, vol. 17(17), pages 1-21, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4434-:d:1471076
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    References listed on IDEAS

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