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

Yuhan Wu, Chun Xiang (), Heng Qian and Peijian Zhou
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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

Energies, 2024, vol. 17, issue 17, 1-21

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.

Keywords: photovoltaic power generation; power prediction; improved snow ablation algorithm; bi-directional long short-term memory; hyper-parameter optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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