Prediction of the Inventory of New Energy Vehicle Charging Piles in China Based on Support Vector Machine

With the rapid development of China's new energy vehicle (NEV) industry, the charging pile, as a key supporting facility, plays a crucial role. Predicting the inventory of charging piles is of great significance for guiding the healthy de-velopment of the industry. This paper employs the Support Vector Machine (SVM) as the prediction model. Firstly, data on the inventory of NEV charging piles in China over the past six years were collected. Combined with factors such as the production and sales volume of NEVs, relevant policies, and public pa-tents, a prediction model was constructed. By selecting appropriate kernel func-tions and adjusting parameters, the SVM model demonstrated good predic-tive performance on the training data. Cross-validation and parameter optimi-zation were conducted to ensure the stability and reliability of the prediction results. The prediction indicates that the inventory of NEV charging piles in China is expected to continue growing in the coming years.