Adaptive forecasting of diverse electrical and heating loads in community integrated energy system based on deep transfer learning

The economic operation and scheduling of community integrated energy system (CIES) depend on accurate day-ahead multi-energy load forecasting. Considering the high randomness, obvious seasonality, and strong correlations between the multiple energy demands of CIES, this paper proposes an adaptive forecasting method for diverse loads of CIES based on deep transfer learning. First, a one-dimensional convolutional neural network (1DCNN) is formulated to extract hour-level local features, and the long short-term memory network (LSTM) is constructed to extract day-level coarse-grained features. In particular, an attention mechanism module is introduced to focus on critical load features. Second, a hard-sharing mechanism is adopted to learn the mutual coupling relationship between diverse loads, where the weather information is added to the shared layer as an auxiliary. Furthermore, considering the differences in the degree of uncertainty of multiple loads, dynamic weights are assigned to different tasks to facilitate their simultaneous optimization during training. Finally, a deep transfer learning strategy is constructed in the forecasting model to guarantee its adaptivity in various scenarios, where the maximum mean discrepancy (MMD) is used to measure the gradual deviation of the load properties and the external environment. Simulation experiments on two practical CIES cases show that compared with the four benchmark models, the electrical and heating load forecasting accuracy (measured by MAPE) increased by at least 4.99 and 18.22%, respectively.