K-ISSA-LSTM PHOTOVOLTAIC POWER PREDICTION BASED ON POWER CHARACTERISTIC

Jin Weiyong; Lu Li&#x02019;na; Lai Huanhuan; Zhang Senlin

doi:10.19912/j.0254-0096.tynxb.2022-1532

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (2) : 429-434. DOI: 10.19912/j.0254-0096.tynxb.2022-1532

K-ISSA-LSTM PHOTOVOLTAIC POWER PREDICTION BASED ON POWER CHARACTERISTIC

Jin Weiyong¹, Lu Li’na², Lai Huanhuan³, Zhang Senlin¹

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Abstract

Improving the accuracy of photovoltaic power prediction is of great value to the stable operation of the power system. The historical power characteristics can reflect the fluctuation of photovoltaic power over a period of time, using clustering algorithms to cluster the raw data, and the long-term short-term memory neural network is used to predict the photovoltaic power generation. At the same time, the improved sparrow search algorithm is used to optimize the hyperparameters of neural networks to realize the hyperparameter optimization of different power feature scenarios. Using the measured data of a photovoltaic power station in East China for verification, the prediction model has higher prediction accuracy than the traditional prediction method in the case of power fluctuation.

Key words

photovoltaic power / forecasting / clustering algorithms / long short-term memory / sparrow search algorithm

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Jin Weiyong, Lu Li’na, Lai Huanhuan, Zhang Senlin. K-ISSA-LSTM PHOTOVOLTAIC POWER PREDICTION BASED ON POWER CHARACTERISTIC[J]. Acta Energiae Solaris Sinica. 2024, 45(2): 429-434 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1532

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