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

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

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...

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Bibliographic Details
Main Authors: Qingquan Lv, Jialin Zhang, Jianmei Zhang, Zhenzhen Zhang, Qiang Zhou, Pengfei Gao, Haozhe Zhang
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Energies
Subjects:
short-term wind power forecast
LSTM neural network
PSO algorithm
forecasting model
Online Access:https://www.mdpi.com/1996-1073/18/13/3346
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Summary: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<sup>2</sup>. The results demonstrate that the PSO-CNN-LSTM model achieves lower RMSE, MAE, and MSE values compared to alternative models. Concurrently, its higher R<sup>2</sup> 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.
ISSN:1996-1073

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