A Region-Level Integrated Energy Load Forecasting Method Based on CNN-LSTM Model with User Energy Label Differentiation

Abstract

In order to further solve the multiple pressures of economy, energy and environment, and improve the utilization efficiency of energy, Integrated energy system has become an important way of energy utilization. The difference of users' energy demand makes the connection between electricity, heat, cold and gas system more close. Accurate energy demand forecasting will be the theoretical and data basis for integrated regional energy system design and energy efficiency assessment. Thus, a regional Integrated energy load forecasting method based on the CNN-LSTM model with user energy label differentiation is proposed in this paper. Analysis based on differential users can use behavior and make the user use the label, and then based on the energy type, weather conditions, such as time period 3 users can use the label as input data, CNN - LSTM model for load forecasting and verify, in which CNN extracts effective input features and LSTM is good at processing time-series data. According to the results based on user can use the label differentiation of CNN - LSTM model at the regional level of comprehensive power load forecasting has good results.