Improved SVM Method for Forecasting Recent Charging Load in Small Electric Vehicle Charging Stations

Electric vehicles have sprung up, and the number of small charging stations in cities is increasing. In order to achieve efficient operation and maintenance, economic management, reasonable location and capacity planning of charging stations, and reduce the impact on the power grid, it is necessary to carry out accurate ultra-short-term load forecasting. In view of the characteristics of the load data of small charging stations, such as time series, nonlinear, low power, and weak periodicity, this paper proposes an improved Northern Goshawk algorithm (INGO) to optimize support vector machine (SVM) for ultra-short-term load forecasting. Firstly, variational mode decomposition (VMD) is used to decompose the time series data of charging load into multiple modal components of different frequencies, so as to better extract the characteristics and structure of time series information. Then, combining the basic meteorological data and holiday data, the Northern Goshawk optimization (NGO) algorithm was improved by Tent mapping and Levy strategy, and the optimal SVM parameters of each component were found to obtain the optimal prediction model. Finally, the components predicted by each SVM were superimposed and combined to obtain the final load-predicted value. The reliability and effectiveness of the proposed method are verified and analyzed on the actual load data of a small charging station and compared with other forecasting models.