Evaluation of Emission Reduction Performance of Power Enterprises Based on Least Squares Support Vector Machine

Abstract

In response to the increasingly severe environmental pollution problem at present, many traditional power companies are facing the promotion of environmental protection work. The promotion of Energy Conservation and Emission Reduction (ECER) work needs sufficient theory and data support. Therefore, this study proposes a performance evaluation model for ECER in power enterprises based on the improved lion swarm algorithm optimized Least Squares Support Vector Machine (LSSVM). The evaluation index system has been reconstructed according to the current environment and era characteristics. The LSSVM has been used to evaluate the ECER work of power enterprises, and the training efficiency of Lion Swarm Optimization (LSO) algorithm has been solved using chaos theory. An improved LSO algorithm is utilized to improve the parameter selection problem of the LSSVM model, to achieve a scientific and objective evaluation of the ECER performance of power enterprises. The research findings denote that the algorithm put forward in this study has better training efficiency, higher accuracy and stability, as well as better response time. It also exhibits minimal errors in simulation experiments. In summary, the ECER performance evaluation model proposed by this research institute can effectively output correct scoring results, providing data support for the promotion of environmental protection work.