气候变化领域集成服务门户

Energy transition is an important factor when dealing with climate change and energy crisis under resource constraints. The performance evaluation of it is significant for improving and promoting the process of energy transition. This paper explores the application of the support vector machine improved by the artificial bee colony algorithm (IABC-SVM) method in the energy transition performance evaluation process. It provides an intelligent evaluation tool for the evaluation of the regional energy transition performance. Firstly, the evaluation indicator system of energy transition is constructed from five dimensions: energy supply, demand, efficiency, institution, and environment. Then, the technique for order preference by a similar to ideal solution improved by a combination weighting (CW-TOPSIS) method and IABC-SVM are constructed. After that, according to the evaluation values of 30 provinces in China calculated by CW-TOPSIS, 10-fold cross validation is used to compare the errors of support vector machine (SVM), support vector machine optimized by the artificial bee colony algorithm (ABC-SVM), and IABC-SVM, which proves the effectiveness and accuracy of IABC-SVM in evaluating the performance of energy transition. Finally, the IABC-SVM is used to evaluate the energy transition performance of 30 provinces in 2016. Through a comparative analysis, the relevant suggestions of energy transition are put forward.