Research on Power Prediction Method for Distributed Photovoltaic Power Generation Systems Based on LSTM Optimized by Grey Wolf Optimizer

Kai-Ming Chen, Yan-Zuo Chang, Yan-Xiao Jia, Yu-Xuan Chen, Hong-Rui Yang, Wen-Min Wen, Zi-Rui He, Jie-Zhen Yang, Yong-Qing Wang, Zheng-Kuan Deng, Guan-Hong Xie

Distributed photovoltaic system, long short-term memory (LSTM), grey wolf optimizer (GWO), time series..

Accurate prediction of the output power of distributed photovoltaic (PV) systems is crucial for achieving efficient renewable energy integration and ensuring stable grid operation. Given that the power output of distributed PV systems is significantly influenced by meteorological factors and exhibits strong randomness and volatility, this study takes a distributed PV system in a region of Guangdong Province as the research object and constructs a PV power generation calculation model based on real meteorological data and system parameters. For the prediction approach, traditional time series methods are first employed as a benchmark for comparison. Subsequently, a GWO-LSTM model is proposed, in which the Grey Wolf Optimizer (GWO) is used to optimize the hyperparameters of the Long Short-Term Memory (LSTM) model. The experimental evaluation employs mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) as performance metrics. The results indicate that the MSE of the GWO-LSTM model is reduced by approximately 85% compared with traditional methods, while the RMSE and MAE are reduced to around 38% and 33% of those of the traditional methods, respectively. This model demonstrates significantly higher prediction accuracy than conventional time series approaches, verifying the effectiveness and superiority of using GWO to optimize LSTM hyperparameters in distributed PV power forecasting.

10.22161/ijaers.134.6