A load forecasting method for charging stations based on K-means Clustering and Markov chain

The charging behavior of electric vehicles has a significant impact on the load time distribution of charging stations, which is directly related to the scheduling of the power grid and energy quality. In order to address the transportation and development trends of electric vehicles in mountainous cities in southwestern China, this study proposes a novel charging station load forecasting method that combines K-means clustering and Markov chain technology. Through verification on the MATLAB platform, we conducted detailed simulations of charging loads at different time periods and comprehensively compared the results with the data of the Monte Carlo model. It is gratifying that our model has demonstrated relatively stable charging load prediction ability, providing a reliable basis for practical applications. This research achievement is of great significance for the planning, resource optimization, operation management, power grid balance, and user experience of charging stations, and helps to improve the efficiency and reliability of charging equipment.