Electricity Substitution Potential Prediction Based on Tent-CSO-CG-SSA-Improved SVM—A Case Study of China

Abstract

Nowadays, fossil energy continues to dominate China’s energy usage; its inefficient use and large crude emissions of coal and fuel oil in its end-consumption have brought about great pressure to reduce emissions. Electrical power substitution as a development strategy is an important step toward achieving sustainable development, the transformation of the end-use energy consumption structure, and double carbon goals. To better guide the broad promotion of electrical power substitution, and to offer theoretical support for its development, this paper quantifies the amount of electrical power substitution and the influencing factors that affect the potential of electrical energy substitution. This paper proposes a hybrid model, combining Tent chaos mapping (Tent), chicken swarm optimization (CSO), Cauchy–Gaussian mutation (CG), the sparrow search algorithm (SSA), and a support vector machine (SVM), as a Tent-CSO-CG-SSA-SVM model, which first uses the method of Tent chaos mapping to initialize the sparrow population in order to increase population diversity and improve the search ability of the algorithm. Then, the CSO is introduced to update the positions of sparrows, and the CG method is introduced to make the algorithm jump out of the local optimum, in order to improve the global search ability of the SSA. Finally, the final electrical power substitution potential prediction model is obtained by optimizing the SVM through a multi-algorithm combination approach. To verify the validity of the model, two regions in China were used as case studies for the prediction analysis of electrical energy substitution potential, and the prediction results were compared with multiple models. The results of the study show that Tent-CSO-CG-SSA-SVM offers a good improvement in prediction accuracy, and that Tent-CSO-CG-SSA-SVM is a promising method for the prediction of electrical power substitution potential.