Robust load feature extraction based secondary VMD novel short-term load demand forecasting framework

Load forecasting, as a crucial component of the electricity market, plays a significant role in ensuring the secure operation and rational planning of the power grid. However, as the power system becomes increasingly intricate, the demands on load forecasting techniques have escalated. Consequently, to mitigate the errors in short-term load forecasting (STLF) caused by uncertainty factors and to accommodate daily forecasting under abnormal electricity load conditions, this paper proposes a hybrid load forecasting model that combines an improved Secondary Variational Mode Decomposition (SVMD) algorithm with the Informer model. Employing electricity load data from the Panama context, the data is divided into four distinct experimental cases. The outcomes manifest that in contrast to the baseline model, the proposed approach engenders a minimal reduction of 15.08%, 12.95%, and 13.21% in Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE), respectively. Furthermore, supplementary experimental results demonstrate that the model exhibits strong robustness.