A Forecasting Method of District Heat Load Based on Improved Wavelet Neural Network

Abstract

Abstract Energy conservation of urban district heating system is an important part of social energy conservation. In response to the situation that the setting of heat load in the system is unreasonable, the heat load forecasting method is adopted to optimize the allocation of resources. At present, the artificial neural networks (ANNs) are generally used to forecast district heat load. In order to solve the problem that networks convergence is slow or even not converged due to the random initial parameters in traditional wavelet neural networks (WNNs), the genetic algorithm with fast convergence ability is used to optimize the network structure and initial parameters of heat load prediction models. The results show that when the improved WNN is applied to forecast district heat load, the prediction error is as low as 2.93%, and the accuracy of prediction results is improved significantly. At the same time, the stability and generalization ability of the prediction model are improved.