Research Article
Research on the Life Cycle Decision Model for the Impact of Power Industry Expansion
@INPROCEEDINGS{10.4108/eai.24-2-2023.2330688, author={Yunjin Huang and Lingling Zhu and Zhiqiang Lan and Jiacheng Wu and Yongjie Guo}, title={Research on the Life Cycle Decision Model for the Impact of Power Industry Expansion}, proceedings={Proceedings of the 2nd International Conference on Engineering Management and Information Science, EMIS 2023, February 24-26, 2023, Chengdu, China}, publisher={EAI}, proceedings_a={EMIS}, year={2023}, month={6}, keywords={power industry expansion; logistic; life cycle; fuzzy set; judgment model}, doi={10.4108/eai.24-2-2023.2330688} }- Yunjin Huang
Lingling Zhu
Zhiqiang Lan
Jiacheng Wu
Yongjie Guo
Year: 2023
Research on the Life Cycle Decision Model for the Impact of Power Industry Expansion
EMIS
EAI
DOI: 10.4108/eai.24-2-2023.2330688
Abstract
In the daily operation and management of electric power companies, the expansion of the power industry is a very critical link, which is easily affected by a variety of factors. In order to further accurately measure the contribution of industry expansion to electricity, determine the life cycle stage of the impact of power industry expansion, and a life cycle decision model for the impact of power industry expansion is studied. Logistic is used to build an evolutionary curve model for the impact of power industry expansion. Based on TICHY's life cycle model of industrial clusters, the evolutionary life cycle stages for the impact of power industry expansion are analyzed. Fuzzy sets are used to describe the characteristics of each stage of the life cycle for the impact of power industry expansion. By determining the fuzzy set and fuzzy relation matrix, the life cycle judgment model is determined based on the fuzzy closeness. The experimental results show that the judgment results of the design model are basically consistent with the direct observation results, and the judgment accuracy is always higher than 90%, the judgment time is less than 25ms, which can effectively determine the life cycle stage of the impact of power industry expansion, and accurately measure the contribution of industry expansion to electricity.
