A systematic method of long-sequence prediction of natural gas supply in IES based on spatio-temporal causal network of multi-energy

Natural gas plays an important role in the energy peak-shaving process of Integrated Energy Systems (IES). Accurate prediction of natural gas supply over longer time scales is one of the most important conditions to ensure the stability and security of IES energy supply. However, as the sequence length of the predicted time series increases, it becomes more difficult to extract valid dependencies from the data. At the same time, there are complex spatio-temporal coupling characteristics of various energy resources in the IES, which makes the construction of long sequence prediction models for natural gas supply very difficult. To solve this problem, we propose a systematic method for long-sequence prediction with natural gas supply in IES based on spatiotemporal causal network of multi-energy. First, the proposed approach uses a causal analysis algorithm to represent the complex coupled spatio-temporal relationships of multiple energy resources in an IES. Then, the natural gas time-series are decomposed through the Variational Modal Decomposition (VMD), to reveal its inherent trend and cyclical characteristics. Finally, the long-sequence prediction model is proposed by fusing the attention mechanism into Graph Convolutional Neural Network (GCN). Attentional mechanisms are introduced to obtain important long-term dependencies in the long sequence prediction process. We use a integrated energy dataset from Spain to test the validity and superiority of the method. The results show that the proposed model in this paper improves the Root Mean Square Error (RMSE) by at least 9.5% and reduces the Mean Absolute Error (MAE) by at least 14.5% compared to several baseline models.