Joint forecasting of multi-energy loads for a university based on copula theory and improved LSTM network

Abstract

In this study, a multi-energy loads forecasting model based on the artificial intelligence method is proposed. Firstly, based on Copula theory, the nonlinear relationship among different load types themselves, as well as other influence factors affecting the load demand such as ambient temperature etc. are analyzed. Following which, the input factors for load forecasting are selected according to the influence degree. Secondly, based on LSTM (Long Short-Term Memory) neural network model and its improved forms (Stack LSTM and Bidirectional LSTM), the multi-type load forecasting model is developed. A university campus is selected to verify the feasibility and validity of the model compared with the actual data. According to the simulation results, the Stack LSTM illustrates better prediction performance and generalization ability compared with other methods. In addition, the prediction accuracy of cooling and heating demands can be highly improved when considering the affection of electrical load. Moreover, the reduction of load time-scale may obviously increase the prediction accuracy.