Short-term load forecasting in smart grids: A CGAN-self data reconstruction and BiTCN-BiGRU-self attention model with demand response optimization

With the rapid development of smart grids, accurate load prediction is essential for stable operation and optimal scheduling. This paper addresses errors, missing values, and anomalies in electrical load data using a conditional generative adversarial network (CGAN) with dual self-attention (SELF) for data reconstruction. The model simplifies time-series complexity and historical load patterns, eliminating the need for intricate spatiotemporal modeling. Based on the reconstructed data, a short-term load forecasting method is proposed using a bidirectional temporal convolutional network (BiTCN), bidirectional gated recurrent unit (BiGRU), and self-attention. This model processes forward and backward time-series information in parallel, extracting multi-scale features for more accurate predictions. In order to accurately describe the response behavior of users under different electricity price differentials, a logistic demand response (DR) model considering time lag factors is introduced. The model defines optimistic and pessimistic response curves, effectively reflecting the actual range of user responses to price incentives, thus enhancing the practicality of load forecasting in decision support. Experimental results demonstrate that the proposed method not only enhances the accuracy and stability of load forecasting but also provides robust technical support for the stable operation of smart grids.