Prediction of Thermal Power Installed Capacity and Power Generation Based on Spearman-PSO and CosHGM(1,1) Model

Thermal power generation predominates as China’s primary electricity generation method. The prediction of the future installed capacity and power generation of thermal power plants is of great significance to the stable operation of the power market. In view of the lack of accurate and efficient prediction methods for the prediction of the future installed capacity of thermal power units, this paper introduces a grey prediction model incorporating the Spearman correlation coefficient method, particle swarm optimization algorithm, and GM(1,1). Compared with the single DGM(1,1) and NGM(1,1,K) models, the model can accurately and efficiently predict the future installed capacity and power generation of thermal motors. The prediction accuracy has been greatly improved, which is conducive to the power dispatching department to make reasonable decisions, this ensures a balance between supply and demand in the power system, enhances operational safety, optimizes resource utilization, reduces operational costs, and enhances the quality and efficiency of power production.