Prediction Method of Beijing Electric-Energy Substitution Potential Based on a Grid-Search Support Vector Machine

Recently, power cuts and coal price surges have been significant concerns of individuals and societies. The main reasons for a power cut are a recent rapid increase in power consumption, shortage of thermal coal or the large shutdown capacity of thermal power units, resulting in a tight power supply in the power grid. In recent years, the shortage of fossil resources has led to frequent energy crises. In the context of carbon peaks and carbon neutralization, how to better develop electric-energy substitution and eliminate the dependence on fossil energy has become a problem that needs to be solved at present. In this paper, the influencing factors of electric-energy substitution in Beijing are analyzed, and the indexes affecting the electric-energy substitution are outlined. By constructing various machine-learning models, the prediction is performed. The results show that the Gaussian kernel support vector machine model based on a grid search has a good prediction effect on the electric-energy substitution potential in Beijing, which has certain guiding significance for electric-energy substitution potential analysis.

Automated summary

This paper analyzes the influencing factors of electric-energy substitution in Beijing and conducts predictive analysis using machine learning models. The results show that the Gaussian kernel support vector machine model has a good prediction effect on the electric-energy substitution potential, providing guidance for the analysis of electric-energy substitution potential.