摘要
Accurate multi-energy load forecasting is a prerequisite for on-demand energy supply in integrated energy systems.However,due to differences in response characteristics and load patterns among electrical,heating,and cooling loads,multi-energy load forecasting faces the challenges of heterogeneous time scales and imbalanced forecasting accuracy across load types.To address these challenges,this paper proposes a multi-task learning model with stacked cross-attention.This model incorporates a time scale alignment module to align the time scales of different loads,and employs Informer encoders as experts to extract load-specific features.Stacked cross-attention as the soft sharing mechanism dynamically fuses expert features at the sequence level,enhancing inter-task collaboration and adaptability.This design improves the overall accuracy of multi-energy load fore-casting with mixed time scales while reducing forecasting imbalance across load types.Comparison results demonstrate that the model with the stacked cross-attention achieves the best forecasting performance and lowers the imbalance index by 79.17%.Furthermore,the experts based on Informer encoders yield over a 30.09%MAPE reduction compared to alternative expert architectures.Compared to the multi-gate mixture-of-experts based models,classical forecasting models,and recent advanced models,the proposed model achieves superior forecasting accuracy,validating its effectiveness and advancement.
基金
supported by National Natural Science Foundation of China(Grant Nos.U22B20112 and 52476001)
Beijing Institute of Technology Research Fund Program for Young Scholars.
