DUST DEPOSITION LOSS PREDICTION OF PHOTOVOLTAIC MODULES BASED ON SIMILAR DAY CLUSTERING AND SSA-LSTM

Li Xinfu; Li Bin; Wang Guangyi; Wu Zhengren; Zhang Jian; Wang Xin

doi:10.19912/j.0254-0096.tynxb.2024-1425

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (12) : 195-203. DOI: 10.19912/j.0254-0096.tynxb.2024-1425

DUST DEPOSITION LOSS PREDICTION OF PHOTOVOLTAIC MODULES BASED ON SIMILAR DAY CLUSTERING AND SSA-LSTM

Li Xinfu, Li Bin, Wang Guangyi, Wu Zhengren, Zhang Jian, Wang Xin

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Abstract

In order to explore the influence of meteorological factors on the dust deposition loss of photovoltaic modules, a prediction model combining similar day clustering, sparrow search algorithm （SSA） and long short-term memory（LSTM） is proposed. Firstly, the main meteorological factors are selected by the Pearson coefficient method. Secondly, the historical data is clustered into three similar day sample sets of sunny, cloudy and rainy days by using the K-means algorithm and prediction models are established respectively. Finally, the LSTM hyperparameters are optimized by SSA. The results show that compared with other models, the SSA-LSTM model has the best prediction effect. And the, a model combination prediction method is proposed. The results show that this method has the characteristics of high prediction accuracy and strong generalization ability, which can provide a reference for the prediction of photovoltaic module dust deposition loss.

Key words

K-means clustering / long short-term memory / forecasting / dust deposition loss / sparrow search algorithm / meteorology

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Li Xinfu, Li Bin, Wang Guangyi, Wu Zhengren, Zhang Jian, Wang Xin. DUST DEPOSITION LOSS PREDICTION OF PHOTOVOLTAIC MODULES BASED ON SIMILAR DAY CLUSTERING AND SSA-LSTM[J]. Acta Energiae Solaris Sinica. 2025, 46(12): 195-203 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1425

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