RESEARCH ON SHORT TERM PHOTOVOLTAIC POWER GENERATION PREDICTION METHOD BASED ON KNN-IDBO-LSTM

Pi Linlin; Tian Liguo

doi:10.19912/j.0254-0096.tynxb.2024-0099

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (5) : 320-330. DOI: 10.19912/j.0254-0096.tynxb.2024-0099

RESEARCH ON SHORT TERM PHOTOVOLTAIC POWER GENERATION PREDICTION METHOD BASED ON KNN-IDBO-LSTM

Pi Linlin^1,2, Tian Liguo¹

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Abstract

A long short-term memory neural network （LSTM） photovoltaic power prediction model based on K-nearest neighbor (KNN) data preprocessing and improved dung beetle algorithm （IDBO） optimization was proposed. Firstly, KNN is used to fill missing data and correct abnormal data, and easily trainable time-series features are extracted; Then, an IDBO-based parameter optimization method for LSTM model was proposed. Based on the original DBO, a uniform population initialization strategy was adopted,and Levy flight was integrated into the dung beetle position iteration. The concept of population density was introduced to dynamically adjust the population size, ensuring global search capability while significantly reducing search time. Finally, the optimized model’s predictive performance was evaluated using data from a photovoltaic array in Australia. The experimental results show that the optimized hyperparameters improved prediction accuracy of the prediction model, and IDBO algorithm achieves convergence faster than other optimization algorithms.

Key words

photovoltaic power generation prediction / long short-term memory neural network / improve the optimization algorithm for dung beetles / data mining

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Pi Linlin, Tian Liguo. RESEARCH ON SHORT TERM PHOTOVOLTAIC POWER GENERATION PREDICTION METHOD BASED ON KNN-IDBO-LSTM[J]. Acta Energiae Solaris Sinica. 2025, 46(5): 320-330 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0099

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