Electric Vehicle Charging Load Forecasting Based on K-Means++-GRU-KSVR

The accurate short-term forecasting of an electric vehicle (EV) load is crucial for the reliable operation of a power grid and for effectively reducing energy consumption. Due to the fluctuations in EV charging loads, particularly the significant load variation between commercial and non-commercial areas, global models often suffer from prediction errors when forecasting loads. To address this issue, this paper proposes a regional forecasting method based on K-means++ clustering and deep learning algorithms. First, the K-means++ algorithm was used to partition the data into different regions, and an independent load-forecasting model was established for each region. Then, a combination of kernel support vector regression (KSVR) and gated recurrent unit (GRU) models was used to handle nonlinear features and time-dependent data, where particle swarm optimization (PSO) further optimized the model parameters to improve the forecasting accuracy. Finally, a weighted summation method was used to integrate the forecast results from each region, resulting in a more accurate overall load forecast. The experimental results show that the proposed model provided better prediction performance by capturing the spatiotemporal characteristics of the EV charging load, effectively addressing the challenges posed by regional differences, and outperforming the single-model forecasts.