Electric load forecasting based on kernel extreme learning machine optimized by improved sparrow search algorithm.

Electric load forecasting's accuracy and reliability are pivotal for enhancing the dispatch efficiency of power systems and the integration of renewable energy into the grid. In response to this need, this paper introduces a novel electric load forecasting framework. Initially, to address the limitations of the Sparrow Search Algorithm (SSA), we propose a multi-strategy improved SSA with dynamic inertia weight (WHFSSA). This enhancement involves balancing the global and local search capabilities by introducing dynamic inertia weights, employing a suboptimal solution guidance strategy to facilitate the escape from local optima, and applying dimension-wise dynamic reverse learning to boost population diversity and quality, thereby hastening convergence. Subsequently, we optimize the parameters of Variational Mode Decomposition (VMD) using the improved SSA (WHFSSA) to achieve precise decomposition of electric load sequences into more regular subsequences. These subsequences are then integrated with the Kernel Extreme Learning Machine (KELM) to develop a forecasting model named WHFSSA-KELM. The efficacy of this model is validated on two electric load datasets across different forecasting tasks. In Dataset 1, the focus is on the impact of temperature and historical load on future load values. The results show that the proposed model realizes an average improvement of 5.7% in the R2 metric compared to benchmark models. In Dataset 2, which considers various additional factors influencing electric load, the model's performance is assessed for three-step-ahead forecasting. It achieves average R2 metric improvements of 5.6%, 7.6%, and 10.9% for the three-step-ahead forecasts, respectively, compared to benchmark models. Consequently, the proposed method offers more accurate forecasting, contributing to the safe and stable operation of power systems.