{"title":"The Detection of Unaccounted for Gas in Residential Natural Gas Customers Using Particle Swarm Optimization-based Neural Networks","authors":"A. Soltanisarvestani, A. Safavi, M. Rahimi","doi":"10.1080/15567249.2022.2154412","DOIUrl":null,"url":null,"abstract":"ABSTRACT One of the most important issues related to natural gas is unaccounted for gas. Residential customers constitute a significant percentage of unaccounted for gas. To estimate the amount of unaccounted for gas, it is necessary to compare the amount of consumption estimated by the model with the one recorded by the meter. Thus, the value estimated by the consumption model are of great importance. Initially, a consumption model is developed for each customer using consumption data for the first 12 months and the average monthly ambient outdoor temperature related to the same time period. The models are developed using artificial neural networks and particle swarm optimization algorithm. The estimates made by the models are then compared with the values recorded by the meters. This method is then implemented on some real data (as the study area). The results show the effectiveness of the proposed method.","PeriodicalId":51247,"journal":{"name":"Energy Sources Part B-Economics Planning and Policy","volume":"123 1","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy Sources Part B-Economics Planning and Policy","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/15567249.2022.2154412","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
ABSTRACT One of the most important issues related to natural gas is unaccounted for gas. Residential customers constitute a significant percentage of unaccounted for gas. To estimate the amount of unaccounted for gas, it is necessary to compare the amount of consumption estimated by the model with the one recorded by the meter. Thus, the value estimated by the consumption model are of great importance. Initially, a consumption model is developed for each customer using consumption data for the first 12 months and the average monthly ambient outdoor temperature related to the same time period. The models are developed using artificial neural networks and particle swarm optimization algorithm. The estimates made by the models are then compared with the values recorded by the meters. This method is then implemented on some real data (as the study area). The results show the effectiveness of the proposed method.
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