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dc.contributor.authorGupta, Ragini
dc.contributor.authorAl-Ali, A. R.
dc.contributor.authorZualkernan, Imran
dc.contributor.authorDas, Sajal K.
dc.date.accessioned2021-04-07T10:20:23Z
dc.date.available2021-04-07T10:20:23Z
dc.date.issued2020
dc.identifier.citationGupta, R., Al-Ali, A. R., Zualkernan, I., & Das, S. K. (2020). Big Data Energy Management, Analytics and Visualization for Residential Areas. IEEE Access, 1–1. https://doi.org/10.1109/access.2020.3019331en_US
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/11073/21391
dc.description.abstractWith the rapid development of IoT based home appliances, it has become a possibility that home owners share with Utilities in the management of home appliances energy consumption. Thus, the proposed work empowers home owners to manage their home appliances energy consumption and allow them to compare their consumption with respect to their local community total consumption. This serves as a nudge in consumer's behavior to schedule their home appliances operation according to their local community consumption profile and trend. Utilizing the same common communication infrastructure, it also allows the utilities on different consumption levels (community, state, country) to monitor and visualize the energy consumption in their respective grid segments on daily, monthly, and yearly basis. A high-speed distributed computing cluster based on commodity hardware with efficient big data mathematical algorithm is employed in this work. To achieve this, two big data processing paradigms are evaluated with a set of qualitative and quantitative metrics with subsequent recommendations. One million smart meter data is simulated to access individual homes. With the utilization of distributed storage and computing cluster for handling energy big data, the utilities can perform consumer load analysis and visualization on a scale of one million consumers. This helps the utilities in providing consumers a more accurate representation of how much energy they are consuming with greater granularity and with respect to their local community. Consumer and Utility centric queries are developed to create a web-based real time energy consumption management system presented in terms of dashboard charts, graphs, and reports that can be accessed by the consumer and utility providers remotely.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.urihttps://doi.org/10.1109/ACCESS.2020.3019331en_US
dc.subjectBig dataen_US
dc.subjectInternet of Things (IoT)en_US
dc.subjectSmart meteren_US
dc.subjectEnergy management systemen_US
dc.titleBig Data Energy Management, Analytics and Visualization for Residential Areasen_US
dc.typePeer-Revieweden_US
dc.typeArticleen_US
dc.typePublished versionen_US
dc.identifier.doi10.1109/access.2020.3019331


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