Abstract: In this paper,we propose a short-term load forecasting model based on Support Vector Regression（SVR）,which takes into account the effects of weather factors and time-of-use tariff factors on load. To begin with,we studied and analyzed the daily load characteristics from January 1,2019 to January 1, 2022 in an area of Jiangsu Province, China.Furthermore,based on the weather factor and considering the time-of-use tariff factor,the SVR model was established,and the SVR model was used for training and forecasting. The experimental results show that the model proposed in this paper has high accuracy in short-term load forecasting,and it can better adapt to the load demand under different weather conditions and different electricity prices based on the weather factor and considering the impact of time-of-use tariff factors on the load.