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.

Optimal Performance and Application for Seagull Optimization Algorithm Using a Hybrid Strategy

“…Using the sigmoid function to improve parameters can better adapt to the nonlinear optimization process of the algorithm and enhance the ability of In order to quantitatively analyze and compare the separation performance of the four algorithms, Table 5 compares the similarity coefficient, performance index (PI) of separated signals, and the SSIM of an output image. The data results in In Equation (32), ρ ij is a similarity index used to compare the source signal with the separated signal. The greater the ρ ij , more effective the separation.…”

“…To solve the above problem, some scholars use chaos search to optimize the initialization sequence [ 21 , 22 , 30 , 31 , 32 ]. Although the diversity and ergodicity of the population are improved to a certain extent, the chaotic map is greatly affected by the initial solution, and the inappropriate initial solution will lead to negative optimization of the algorithm [ 33 ].…”

Dynamic assessment of China’s re-electrification level considering carbon peaking and carbon neutrality goals: A study based on functional data analysis

“…In addition, to predict the potential of electric power substitution, some studies use advanced models to analyze and predict the future level of electric power substitution in China (Tan et al, 2012;Li and Chen, 2018;Wu et al, 2019;Geng et al, 2022). Liu et al (2018) forecast the total terminal energy consumption and the proportion of electric energy consumption in terminal energy consumption based on the logistic model, which supplements the medium and long-term load forecasting method.…”