Multi-energy load short-term forecasting of integrated energy system based on ATT-TCGNN
In an integrated energy system,there are complex and strong coupling relationships between the multi-energy loads,and multi-energy loads have strong volatility and randomness.In view of the above characteristics,a multi-energy load short-term forecasting model based on graph neural network,attention mechanism and variational mode decomposition is proposed.Firstly,the variational mode decomposition of multi-energy loads is carried out to weaken the volatility and randomness.Then through the graph learning network improved by the attention mecha-nism,a graph structure that fully reflects the coupling connection of multi-energy loads and the correlation between multi-energy loads and meteorology is established,and the graph prediction network is used to analyze the graph structure and the historical data of multi-energy loads to realize the prediction of multi-energy loads.Finally,the proposed model is compared with other models based on the actual data of Arizona State University.The results show that the proposed model has higher prediction accuracy.
integrated energy systemmulti-energy load forecastingshort-termgraph neural networkattention mechanismvariational mode decomposition
万方数据
维普
