Short-term Forecast of Multiple Loads in Integrated Energy System Based on IPSO-WNN

Accurate short-term energy load forecasting has a considerable influence on the economic scheduling and optimal operation of integrated energy system. This study proposes an improved particle swarm optimization-wavelet neural network (IPSO-WNN) method for short-term load forecasting of integrated energy system. First, Kendall rank correlation coefficient in Copula theory is used to analyze the correlation among the influencing factors, through which the influencing factors with strong correlation are selected as input variables of the model. Secondly, chaos algorithm and adaptive weight selection strategy are introduced in the POS-WNN forecasting model to improve the prediction accuracy. Therefore, a short-term load forecasting model of integrated energy system based on IPSO-WNN is established. Finally, the analysis of examples shows that the load prediction accuracy is significantly improved based on the IPSO-WNN model compared with the traditional forecasting model.