S. A. Chen, A. Vishwanath, Saket K. Sathe, S. Kalyanaraman
{"title":"Shedding Light on the Performance of Solar Panels: A Data-Driven View","authors":"S. A. Chen, A. Vishwanath, Saket K. Sathe, S. Kalyanaraman","doi":"10.1145/2897350.2897354","DOIUrl":null,"url":null,"abstract":"The significant adoption of solar photovoltaic (PV) systems in both commercial and residential sectors has spurred an interest in monitoring the performance of these systems. This is facilitated by the increasing availability of regularly logged PV performance data in recent years. In this paper, we present a data-driven framework to systematically characterise the relationship between performance of an existing photovoltaic (PV) system and various environmental factors. We demonstrate the efficacy of our proposed framework by applying it to a PV generation dataset from a building located in northern Australia. We show how, in light of limited site-specific weather information, this data set may be coupled with publicly available data to yield rich insights on the performance of the building's PV system.","PeriodicalId":90050,"journal":{"name":"SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining","volume":"21 1","pages":"24-36"},"PeriodicalIF":0.0000,"publicationDate":"2016-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2897350.2897354","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
The significant adoption of solar photovoltaic (PV) systems in both commercial and residential sectors has spurred an interest in monitoring the performance of these systems. This is facilitated by the increasing availability of regularly logged PV performance data in recent years. In this paper, we present a data-driven framework to systematically characterise the relationship between performance of an existing photovoltaic (PV) system and various environmental factors. We demonstrate the efficacy of our proposed framework by applying it to a PV generation dataset from a building located in northern Australia. We show how, in light of limited site-specific weather information, this data set may be coupled with publicly available data to yield rich insights on the performance of the building's PV system.