SHORT-TERM PHOTOVOLTAIC POWER PREDICTION BASED ON K-MEDOIDS-GBDT-PSO-LSTM COMBINED MODEL

Dai Zhaohui; Chen Hao; Liu Xinyi; Xia Changqing; Guo Jiayi; Yu Lijun

doi:10.19912/j.0254-0096.tynxb.2023-1509

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (1) : 654-661. DOI: 10.19912/j.0254-0096.tynxb.2023-1509

SHORT-TERM PHOTOVOLTAIC POWER PREDICTION BASED ON K-MEDOIDS-GBDT-PSO-LSTM COMBINED MODEL

Dai Zhaohui¹, Chen Hao², Liu Xinyi³, Xia Changqing², Guo Jiayi², Yu Lijun¹

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Abstract

In this work, a novel approach for predicting photovoltaic power is proposed, which aims to ensuring the stability of the power grid and maintaining a balance between supply and demand. The proposed model integrates the K-medoids clustering algorithm, gradient boosting decision trees （GBDT）, particle swarm optimization （PSO）, and long short-term memory （LSTM） neural networks. Firstly, the K-medoids clustering algorithm is utilized to classify weather data from a large-scale photovoltaic power generation dataset into three different weather types： sunny, cloudy, and rainy/snowy. Afterwards, the feature engineering approach is employed to the extension of existing dataset, and GBDT is employed to analyze the importance of various features, thereby identifying the significant factors influencing photovoltaic power prediction, and further construct an optimized dataset with suitable size. Finally, the reconstructed dataset is further used to train an LSTM model, which is optimized using the PSO algorithm, thus establishing an accurate short-term prediction model. Experimental findings demonstrate that the proposed model achieves a higher prediction accuracy. Moreover, when compared to a single LSTM model, the RMSE indicator is reduced by 12.19% specifically under rainy weather conditions.

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photovoltaic power / power forecasting / machine learning / long short-term memory / optimization algorithms / particle swarm algorithm

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Dai Zhaohui, Chen Hao, Liu Xinyi, Xia Changqing, Guo Jiayi, Yu Lijun. SHORT-TERM PHOTOVOLTAIC POWER PREDICTION BASED ON K-MEDOIDS-GBDT-PSO-LSTM COMBINED MODEL[J]. Acta Energiae Solaris Sinica. 2025, 46(1): 654-661 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1509

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