PHOTOVOLTAIC POWER PREDICTION MODEL BASED ON IGWO-LSTM

Xue Yang; Yan Yucheng; Jia Wei; Heng Yuxi; Zhang Shuxiang; Qin Yao

doi:10.19912/j.0254-0096.tynxb.2022-0320

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (7) : 207-213. DOI: 10.19912/j.0254-0096.tynxb.2022-0320

PHOTOVOLTAIC POWER PREDICTION MODEL BASED ON IGWO-LSTM

Xue Yang¹, Yan Yucheng¹, Jia Wei², Heng Yuxi¹, Zhang Shuxiang¹, Qin Yao¹

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Abstract

Improving the accuracy of PV power prediction is important for improving the operational efficiency of PV power plants and ensuring the safety and stability of grid-connected PV operation. Therefore, an improved gray wolf algorithm （IGWO） based on improved adaptive factor and elite backward learning strategy is proposed to optimize the long short-term memory network （LSTM） prediction model. The IGWO is used to optimize the LSTM fully connected layer parameters and build a combined IGWO-LSTM model to predict PV power, which has better convergence speed and solution efficiency, and also can effectively avoid local optimal solutions. Finally, based on the simulation of real-time data from a PV power station in Changzhou, the experimental results show that the IGWO-LSTM has more accuracy than the LSTM PV power prediction.

Key words

PV power generation / long short-term memory / optimization / gray wolf optimizer / elite backward learning strategy

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Xue Yang, Yan Yucheng, Jia Wei, Heng Yuxi, Zhang Shuxiang, Qin Yao. PHOTOVOLTAIC POWER PREDICTION MODEL BASED ON IGWO-LSTM[J]. Acta Energiae Solaris Sinica. 2023, 44(7): 207-213 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0320

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