Energies (Sep 2024)

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

  • Yuhan Wu,
  • Chun Xiang,
  • Heng Qian,
  • Peijian Zhou

DOI
https://doi.org/10.3390/en17174434
Journal volume & issue
Vol. 17, no. 17
p. 4434

Abstract

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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.

Keywords