Big Data Energy Management, Analytics and Visualization for Residential Areas
With 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 compar...
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| Format: | article |
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2020
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| Online Access: | http://hdl.handle.net/11073/21391 |
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| _version_ | 1864513438395400192 |
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| author | Gupta, Ragini |
| author2 | Al-Ali, A. R. Zualkernan, Imran Das, Sajal K. |
| author2_role | author author author |
| author_facet | Gupta, Ragini Al-Ali, A. R. Zualkernan, Imran Das, Sajal K. |
| author_role | author |
| dc.creator.none.fl_str_mv | Gupta, Ragini Al-Ali, A. R. Zualkernan, Imran Das, Sajal K. |
| dc.date.none.fl_str_mv | 2020 2021-04-07T10:20:23Z 2021-04-07T10:20:23Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Gupta, 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.2020.3019331 2169-3536 http://hdl.handle.net/11073/21391 10.1109/access.2020.3019331 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | https://doi.org/10.1109/ACCESS.2020.3019331 |
| dc.subject.none.fl_str_mv | Big data Internet of Things (IoT) Smart meter Energy management system |
| dc.title.none.fl_str_mv | Big Data Energy Management, Analytics and Visualization for Residential Areas |
| dc.type.none.fl_str_mv | Peer-Reviewed Published version info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | With 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. |
| format | article |
| id | aus_f087f8befe256843c7e887b1f17e4b7d |
| identifier_str_mv | Gupta, 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.2020.3019331 2169-3536 10.1109/access.2020.3019331 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/21391 |
| publishDate | 2020 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
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| spelling | Big Data Energy Management, Analytics and Visualization for Residential AreasGupta, RaginiAl-Ali, A. R.Zualkernan, ImranDas, Sajal K.Big dataInternet of Things (IoT)Smart meterEnergy management systemWith 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.American University of SharjahIEEE2021-04-07T10:20:23Z2021-04-07T10:20:23Z2020Peer-ReviewedPublished versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfGupta, 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.2020.30193312169-3536http://hdl.handle.net/11073/2139110.1109/access.2020.3019331en_UShttps://doi.org/10.1109/ACCESS.2020.3019331oai:repository.aus.edu:11073/213912024-08-22T12:07:16Z |
| spellingShingle | Big Data Energy Management, Analytics and Visualization for Residential Areas Gupta, Ragini Big data Internet of Things (IoT) Smart meter Energy management system |
| status_str | publishedVersion |
| title | Big Data Energy Management, Analytics and Visualization for Residential Areas |
| title_full | Big Data Energy Management, Analytics and Visualization for Residential Areas |
| title_fullStr | Big Data Energy Management, Analytics and Visualization for Residential Areas |
| title_full_unstemmed | Big Data Energy Management, Analytics and Visualization for Residential Areas |
| title_short | Big Data Energy Management, Analytics and Visualization for Residential Areas |
| title_sort | Big Data Energy Management, Analytics and Visualization for Residential Areas |
| topic | Big data Internet of Things (IoT) Smart meter Energy management system |
| url | http://hdl.handle.net/11073/21391 |