Short-Term Power Load Forecasting Using an Improved Model Integrating GCN and Transformer

Improving the accuracy of power load forecasting is an important link in the optimization of power systems. Most of the existing studies in the short-term load forecasting task at present have the problem of insufficient extraction of multi-scale features. Therefore, in order to improve prediction accuracy, this study designs a short-term power load forecasting model integrating multi-scale GCN and the improved Transformer, as well as the prediction method based on this model. First, multi-feature power load data were collected. Second, the random forest algorithm was used to preprocess the data. Next, multi-scale GCN was utilized to model the multi-scale spatio-temporal features in the power load data. The data processed by the multi-scale GCN were input into the improved Transformer module based on MLLA to extract long-term temporal dependencies. Subsequently, comparative experiments and ablation experiments were conducted on three public power datasets. The experimental results show that, compared to the comparative model, for the ETTh1 dataset, the RMSE index of this model decreased by up to 0.314, the MAE decreased by up to 0.304, and the R2 index result improved by up to 9.45%. For the ETTm1 dataset, the RMSE index of this model decreased by up to 0.266, the MAE decreased by up to 0.231, and the R2 index result improved by up to 3.3%. For the Australian dataset, the RMSE index of this model decreased by up to 494.366, the MAE decreased by up to 493.127, and the R2 index result improved by up to 54%, verifying the superiority and effectiveness of the proposed model.