Effective analysis of the potential for electric energy substitution is significant for formulating development strategies and promoting local energy conservation and emission reduction. Analyzing the development trends of electric energy substitution under different scenarios can provide a scientific basis for regional planning. An improved particle swarm optimization-support vector machine model is proposed for predicting the potential of electric energy substitution under multiple scenarios. It analyzes indicators influencing electric energy substitution potential

such as the proportion of electricity consumption

energy consumption per unit of GDP

disposable income of urban residents

CO2 emissions per unit of GDP

and quantifies these indicators. Pearson correlation coefficient method is used to screen and introduce indicators into the prediction model. Four development scenarios—baseline development

technological progress

economic development

and low-carbon environmental protection are considered for predicting the potential for electric energy substitution. Actual data from a province in southern China is analyzed

comparing results with grey wolf optimizer-support vector machine（GWO-SVM）and SVM models

validating that the proposed method demonstrates good predictive performance. The electric energy substitution potential in 2030 and 2035 under various scenarios is also analyzed

providing theoretical support for future regional electric energy substitution planning.