A novel short-term multi-energy load forecasting method for integrated energy system based on feature separation-fusion technology and improved CNN

Integrated energy system (IES) is an important way for energy structure transition and development. Considering the characteristics of large data volume, strong randomness, and multi-energy coupling of IES, this paper proposes a novel short-term multi-energy load forecasting method for IES based on feature separation-fusion technology and improved CNN. Firstly, based on the distribution pattern of pixels in static images, the irregular multi-energy load is reconstructed into 3D load pixel matrix, giving them certain correlation features in both horizontal and vertical directions respectively. Secondly, the feature separation-fusion technology is employed to differentially process distinct features based on their information value differences. Finally, the extracted features are combined and input into a multi-task learning framework with BiLSTM as the shared layer. The hard parameter sharing mechanism is employed to learn the IES multi-coupling information and extract temporal characteristics of the load sequence through BiLSTM. In particular, three different structures of fully connected neural network are designed as feature interpretation modules to accommodate the different prediction requirements of various loads. The simulation results show that the proposed model achieves a weighted mean accuracy of 98.01% during winter days, with an average standard deviation of relative error as low as 0.0242. Among all the contrast models, it exhibits better prediction accuracy and stable error distribution.