Short-Term Wind Power Prediction Model Based on PSO-CNN-LSTM

Qingquan Lv (), Jialin Zhang, Jianmei Zhang, Zhenzhen Zhang, Qiang Zhou, Pengfei Gao and Haozhe Zhang
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Qingquan Lv: Electric Power Science Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730070, China
Jialin Zhang: Electric Power Science Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730070, China
Jianmei Zhang: Electric Power Science Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730070, China
Zhenzhen Zhang: Electric Power Science Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730070, China
Qiang Zhou: Electric Power Science Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730070, China
Pengfei Gao: Electric Power Science Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730070, China
Haozhe Zhang: Electric Power Science Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730070, China

Energies, 2025, vol. 18, issue 13, 1-18

Abstract: Power is fundamental to modern energy systems. As a key renewable source, wind energy’s inherent fluctuations pose significant challenges to power grid operation. The accurate forecasting of wind power integration is therefore essential to enhance grid stability, optimize renewable utilization, and advance cleaner energy transitions alongside sustainable energy development. To improve short-term wind power prediction accuracy, this study constructs a hybrid particle swarm optimization (PSO)-CNN-LSTM model for seasonal forecasting. To explicitly address seasonal impacts, the model design incorporates four-season dataset partitioning (spring, summer, autumn, winter), with prediction validity systematically verified per season. The predictive performance of the proposed PSO-CNN-LSTM hybrid algorithm is evaluated against benchmark models using four statistical metrics: RMSE, MAE, MSE, and R 2 . The results demonstrate that the PSO-CNN-LSTM model achieves lower RMSE, MAE, and MSE values compared to alternative models. Concurrently, its higher R 2 value indicates superior alignment between model predictions and the dataset. A comparative analysis of the four models confirms that the PSO-CNN-LSTM framework delivers precise seasonal power generation forecasts with enhanced adaptability and higher prediction accuracy.

Keywords: short-term wind power forecast; LSTM neural network; PSO algorithm; forecasting model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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