The Long-term Forecasting Method for Electric Vehicle Ownership Considering Multiple Influencing Factors

As an increasing number of vehicles are integrated into the power grid as electrical loads, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> they inevitably pose challenges to the power system. This paper first analyzes the main influencing factors of the electric vehicle industry at different development stages based on the industry life cycle theory. Next, it examines the characteristics of three main influencing factors, includes policy regulation, technological maturity, economic environment, incorporates them into corresponding vehicle ownership forecasting models, including the Grey Prediction Model, Logistic Model and Gompertz Model. Furthermore, considering the changing influence of each factor at different stages, it adjusts the weight coefficients of each model in different stages to achieve a comprehensive electric vehicle ownership forecast based on multiple factors. Finally, by calculating the relative errors between the actual values for the years 2023 and 2024, the paper demonstrates that this method offers better accuracy and reliability compared to methods considering only a single factor.