Research of Short-Term Wind Power Generation Forecasting Based on mRMR-PSO-LSTM Algorithm

Abstract

A novel short-term wind power forecasting method called mRMR-PSO-LSTM was proposed to address the limitations of traditional methods in ignoring the redundancy and temporal dynamics of meteorological features. The methods employed the Minimum Redundancy Maximum Relevance (mRMR) algorithm to select relevant meteorological features while minimizing redundancy. Additionally, the Particle Swarm Optimization (PSO) algorithm was utilized to optimize the parameters of the Long Short-Term Memory (LSTM) network, thereby enhancing its forecasting accuracy. Experimental results demonstrated that the proposed mRMR-PSO-LSTM outperforms FNN, GRU, and PSO-LSTM in four key evaluation metrics.