E. A. H. Abdalla, P. Nallagownden, N. M. Nor, M. Romlie, M. Abdalsalam, M. S. Muthuvalu
{"title":"Intelligent approach for optimal energy management of chiller plant using fuzzy and PSO techniques","authors":"E. A. H. Abdalla, P. Nallagownden, N. M. Nor, M. Romlie, M. Abdalsalam, M. S. Muthuvalu","doi":"10.1109/ICIAS.2016.7824050","DOIUrl":null,"url":null,"abstract":"This paper discusses the optimal energy management of chiller plant. Two intelligent approaches have been employed. Fuzzy is used to adjust the set-point and, while PSO is utilized to optimize the objective function after setting by Fuzzy. Moreover, Fuzzy is also utilized to adjust weighting factors in order to find the best values for the PSO local and global. This will improve PSO performance. The proposed method was combined two levels as Fuzzified PSO, and the model has been simulated and validated by a real case study which consists of 5 electric-driven chillers. The results have shown that the effectiveness of the proposed method compared to the conventional one, and it also has demonstrated a better power saving.","PeriodicalId":247287,"journal":{"name":"2016 6th International Conference on Intelligent and Advanced Systems (ICIAS)","volume":"356 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 6th International Conference on Intelligent and Advanced Systems (ICIAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIAS.2016.7824050","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
This paper discusses the optimal energy management of chiller plant. Two intelligent approaches have been employed. Fuzzy is used to adjust the set-point and, while PSO is utilized to optimize the objective function after setting by Fuzzy. Moreover, Fuzzy is also utilized to adjust weighting factors in order to find the best values for the PSO local and global. This will improve PSO performance. The proposed method was combined two levels as Fuzzified PSO, and the model has been simulated and validated by a real case study which consists of 5 electric-driven chillers. The results have shown that the effectiveness of the proposed method compared to the conventional one, and it also has demonstrated a better power saving.