Multi-energy load forecasting via hierarchical multi-task learning and spatiotemporal attention

Accurate multi-load forecasting is a crucial prerequisite for the steady and effective on-demand operation of integrated energy systems. Due to the intricate interdependencies between different energy sources and spatial and temporal dynamic variations, however, existing methodologies often struggle with data integration, model complexity, and capturing cross-energy correlations. To address these issues, the multi-energy load forecasting problem is transformed into a hierarchical multi-task learning task, resolved by using a spatiotemporal attention mechanism and a gated temporal convolutional network. Initially, the coupling relationships between different energy sources are analyzed both qualitatively and quantitatively to identify the coupling relationships between different energy loads. Subsequently, the identified couplings serve as guidance for designing a hierarchical multi-task learning network by distinguishing between relevant and less relevant loads. Moreover, by incorporating spatiotemporal attention, a gated temporal convolutional network with hierarchical structure is devised to achieve multi-task learning. Finally, the effectiveness of the proposed method is validated through multi-energy consumption data spanning 14 months from an energy management system. Compared to the several baseline methods, the proposed method reduces the mean absolute percentage error by up to 25.09%, 25.96%, and 17.46% on cooling, heating, and electrical load forecasting tasks. The positive experimental results and interpretable analysis demonstrate the promising application prospects of the proposed method in multi-energy load forecasting and demand-side management.