Achieving high precision and balanced multi-energy load forecasting with mixed time scales: a multi-task learning model with stacked cross-attention

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 forecasting 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.