Short-term multi-energy load forecasting for integrated energy systems based on CNN-BiGRU optimized by attention mechanism - Niu, Dongxiao; Yu, Min; Sun, Lijie; Gao, Tian; Wang, Keke (Applied energy, 2022)
•The dynamic coupling relationship among multi-energy loads was analyzed.•The CNN-BiGRU-Attention model was proposed for multi-energy loads forecasting.•Hard weight sharing can extract the inherent multi-energy coupling relationship.•A novel multi-task loss function weight optimization method was applied. Accurate short-term multi-energy load forecasting is an essential prerequisite for ensuring the reliable and economic operation of integrated energy systems (IES). Considering the large fluctuations, strong randomness, and the multi-energy coupling relationship of regional IES, this paper proposes a novel short-term multi-energy load forecasting method based on a CNN-BiGRU model that is optimized by attention mechanism. First, the dynamic coupling relationship between multi-energy loads is qualitatively analyzed, and the influencing factors of multi-energ loads are screened based on data-driven analysis. Second, a one-dimensional CNN layer is formulated to extract complex high-dimensional features, and BiGRU is constructed to extract the time dependence from historical sequences. In particular, three attention mechanism modules are introduced to the BiGRU hidden state through the mapping weight and learning parameter matrix to enhance the impact of key information. Then, hard weight sharing is adopted to extract the inherent multi-energy coupling relationship. Finally, a novel multi-task loss function weight optimization method is applied to search for the optimal multi-task weight, which is used to balance multi-task learning (MTL) to achieve the optimization of the overall forecasting model. To validate the effectiveness of the CNN-BiGRU-Attention MTL model with loss function optimization, this paper compares the proposed model with five benchmark models by MAPE, RMSE, MAE, R2, and computational time. Compared with the traditional LSTM model, the cooling, heat, and electrical load forecasting accuracy (measured by MAPE) of the proposed hybrid model increased by 61.86%, 73.03%, and 63.39%, respectively, which demonstrates that the proposed model exhibits superior performance.