Jiajian Zheng, Guo-qiang Han, Shouyong Yang, Shujuan Tan
{"title":"用户侧能耗监测点布置优化研究","authors":"Jiajian Zheng, Guo-qiang Han, Shouyong Yang, Shujuan Tan","doi":"10.1109/ICAICA52286.2021.9498073","DOIUrl":null,"url":null,"abstract":"In industrial production, the consumption of electric energy is very large. Most enterprises have the problem of repeated monitoring of energy consumption, so the optimization of energy consumption monitoring points has its significance. Firstly, the monitoring points were selected according to the energy efficiency fluctuation coefficient. Then, according to the electrical wiring mode, the proposed monitoring points were optimized for the second time by using the Quantum-behaved Particle Swarm Optimization (QPSO). Finally, the concept of maximum redundancy is put forward to screen and optimize the proposed monitoring points, and the experiment is carried out in an enterprise. The results show that after the first two steps of optimization, the number of monitoring points can be significantly decreased. After the third step of optimization, a more reasonable scheme can be selected under the condition of the same number of monitoring points.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Research on optimization of energy consumption monitoring point layout on user side\",\"authors\":\"Jiajian Zheng, Guo-qiang Han, Shouyong Yang, Shujuan Tan\",\"doi\":\"10.1109/ICAICA52286.2021.9498073\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In industrial production, the consumption of electric energy is very large. Most enterprises have the problem of repeated monitoring of energy consumption, so the optimization of energy consumption monitoring points has its significance. Firstly, the monitoring points were selected according to the energy efficiency fluctuation coefficient. Then, according to the electrical wiring mode, the proposed monitoring points were optimized for the second time by using the Quantum-behaved Particle Swarm Optimization (QPSO). Finally, the concept of maximum redundancy is put forward to screen and optimize the proposed monitoring points, and the experiment is carried out in an enterprise. The results show that after the first two steps of optimization, the number of monitoring points can be significantly decreased. After the third step of optimization, a more reasonable scheme can be selected under the condition of the same number of monitoring points.\",\"PeriodicalId\":121979,\"journal\":{\"name\":\"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)\",\"volume\":\"63 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAICA52286.2021.9498073\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAICA52286.2021.9498073","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on optimization of energy consumption monitoring point layout on user side
In industrial production, the consumption of electric energy is very large. Most enterprises have the problem of repeated monitoring of energy consumption, so the optimization of energy consumption monitoring points has its significance. Firstly, the monitoring points were selected according to the energy efficiency fluctuation coefficient. Then, according to the electrical wiring mode, the proposed monitoring points were optimized for the second time by using the Quantum-behaved Particle Swarm Optimization (QPSO). Finally, the concept of maximum redundancy is put forward to screen and optimize the proposed monitoring points, and the experiment is carried out in an enterprise. The results show that after the first two steps of optimization, the number of monitoring points can be significantly decreased. After the third step of optimization, a more reasonable scheme can be selected under the condition of the same number of monitoring points.