FTimeDD: A Time–Frequency Collaborative Model for Multi-Energy Load Forecasting

With the global energy transition, Integrated Energy Systems (IESs) improve efficiency by coordinating multiple energy sources, including electricity, cooling, and heating. Accurate load forecasting is essential for reliable energy system operation. However, multi-energy loads show complex coupling, non-stationarity, and long-term dependencies. These characteristics pose significant challenges to forecasting tasks. Existing methods have improved short-term forecasting accuracy, but still struggle to jointly capture long-term trends and local fluctuations. To address these issues, this paper proposes FTimeDD, a time–frequency collaborative model for multi-energy load forecasting in IESs. It adopts a dual-path decoupling architecture. The time-domain path separates trend and fluctuation components, while the frequency-domain path extracts dominant periodic features. The two paths are then fused to predict electricity, cooling, and heating loads. Experiments on the ASU Integrated Energy System dataset show that FTimeDD performs well across different forecasting horizons. Compared with the strongest baseline for each metric and horizon, FTimeDD reduces MAE, RMSE, and MAPE by 3.85%, 2.48%, and 1.91% on average, respectively. The method improves forecasting accuracy under the adopted experimental setting while maintaining a compact model scale and low computational cost.