Rahul Sinha, S. Spoorthy, Prerna Khurana, M. Chandra
{"title":"Power system load data models and disaggregation based on sparse approximations","authors":"Rahul Sinha, S. Spoorthy, Prerna Khurana, M. Chandra","doi":"10.1109/INDIN.2016.7819175","DOIUrl":null,"url":null,"abstract":"The deployment of smart meters by utilities holds the promise of improvements in operational efficiency, reliability and cost savings. With power measurements from smart meters, utilities can deploy innovative programs that allow end users to better control their energy usage while simultaneously reducing peak demand across the grid. In this paper, to develop data analysis tools for applications enabling monitoring and control of energy, a systems approach is taken, comprising of modeling, measurement, calibration and inference on the energy data collected from end users. A combination of analysis and synthesis for deriving data and measurement models calibrated to the aggregate power under measurement allows detection and estimation of features of individual appliances. Test results on disaggregation of power waveforms using the publicly available REDD data sets show promising results. The generic modeling and optimization framework can be used in the design and deployment of cyber physical energy systems for monitoring and control of energy resources.","PeriodicalId":421680,"journal":{"name":"2016 IEEE 14th International Conference on Industrial Informatics (INDIN)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 14th International Conference on Industrial Informatics (INDIN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDIN.2016.7819175","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The deployment of smart meters by utilities holds the promise of improvements in operational efficiency, reliability and cost savings. With power measurements from smart meters, utilities can deploy innovative programs that allow end users to better control their energy usage while simultaneously reducing peak demand across the grid. In this paper, to develop data analysis tools for applications enabling monitoring and control of energy, a systems approach is taken, comprising of modeling, measurement, calibration and inference on the energy data collected from end users. A combination of analysis and synthesis for deriving data and measurement models calibrated to the aggregate power under measurement allows detection and estimation of features of individual appliances. Test results on disaggregation of power waveforms using the publicly available REDD data sets show promising results. The generic modeling and optimization framework can be used in the design and deployment of cyber physical energy systems for monitoring and control of energy resources.