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dc.contributor.authorWijesinghe, V-
dc.contributor.authorPerera, M-
dc.contributor.authorPeiris, C-
dc.contributor.authorVidyaratne, P-
dc.contributor.authorNawinna, D. P-
dc.contributor.authorWijekoon, J-
dc.date.accessioned2022-04-29T07:18:39Z-
dc.date.available2022-04-29T07:18:39Z-
dc.date.issued2021-03-06-
dc.identifier.citationV. Wijesinghe, M. Perera, C. Peiris, P. Vidyaratne, D. Nawinna and J. Wijekoon, "Power Profiling:Assessment of Household Energy Footprints," 2021 5th International Conference on Green Energy and Applications (ICGEA), 2021, pp. 9-13, doi: 10.1109/ICGEA51694.2021.9487631.en_US
dc.identifier.isbn978-1-7281-7719-9-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/2110-
dc.description.abstractReduced energy footprint is considered an indicator of efficiency around the world. Having insights into electricity consumption behavior of individuals or families across the day is very useful in efficient management of electricity. In this paper, we present s study that focused on identifying patterns in the monthly electricity consumption profiles of a single household with the K-means clustering algorithm. The data required for this study was collected through a survey in the Sri Lankan context. The survey mainly captured the factors affecting electricity consumption. After proving the demand of electricity is dependable on the data that has been collected, they will be keyed into data models/ profiles that will be built using clustering algorithms. A load profile will be designed using K-means to identify usage patterns of a household on a monthly basis. The parameters that affect the electricity consumption were tested and trained using the SVM algorithm. The outcomes of this study include; identifying the factors contributing to the electricity consumption, identifying electricity consumption patterns, identifying the energy footprint of individuals or families and predicting the future electricity requirements. The results of this study provide many advantages for both consumers and suppliers in efficient management of electricity. It also provides significant impacts in both micro and macro levels through enabling efficient decision-making regarding management of electricity.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2021 5th International Conference on Green Energy and Applications (ICGEA);Pages 9-13-
dc.subjectPower Profilingen_US
dc.subjectAssessmenten_US
dc.subjectHousehold Energyen_US
dc.subjectFootprintsen_US
dc.titlePower Profiling: Assessment of Household Energy Footprintsen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICGEA51694.2021.9487631en_US
Appears in Collections:Department of Computer systems Engineering-Scopes
Research Papers - Dept of Computer Systems Engineering
Research Papers - IEEE
Research Papers - SLIIT Staff Publications

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