An Informer Model for Very Short-Term Power Load Forecasting

Abstract

Facing the decarbonization trend in power systems, there appears to be a growing requirement on agile response and delicate supply from electricity suppliers. To accommodate this request, it is of key significance to precisely extrapolate the upcoming power load, which is well acknowledged as VSTLF, i.e., Very Short-Term Power Load Forecasting. As a time series forecasting problem, the primary challenge of VSTLF is how to identify potential factors and their very long-term affecting mechanisms in load demands. With the help of a public dataset, this paper first locates several intensely related attributes based on Pearson’s correlation coefficient and then proposes an adaptive Informer network with the probability sparse attention to model the long-sequence power loads. Additionally, it uses the Shapley Additive Explanations (SHAP) for ablation and interpretation analysis. The experiment results show that the proposed model outperforms several state-of-the-art solutions on several metrics, e.g., 18.39% on RMSE, 21.70% on MAE, 21.24% on MAPE, and 2.11% on R2.