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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Autores principales:	Ragini Gupta, A. R. Al-Ali, Imran A. Zualkernan, Sajal K. Das
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	Big data IoT smart meter energy management system Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/595e71e9e5ae41cfaa4c07d8f828ec00
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id	oai:doaj.org-article:595e71e9e5ae41cfaa4c07d8f828ec00
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spelling	oai:doaj.org-article:595e71e9e5ae41cfaa4c07d8f828ec002021-11-19T00:06:08ZBig Data Energy Management, Analytics and Visualization for Residential Areas2169-353610.1109/ACCESS.2020.3019331https://doaj.org/article/595e71e9e5ae41cfaa4c07d8f828ec002020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9176994/https://doaj.org/toc/2169-3536With 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.Ragini GuptaA. R. Al-AliImran A. ZualkernanSajal K. DasIEEEarticleBig dataIoTsmart meterenergy management systemElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 156153-156164 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Big data IoT smart meter energy management system Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Big data IoT smart meter energy management system Electrical engineering. Electronics. Nuclear engineering TK1-9971 Ragini Gupta A. R. Al-Ali Imran A. Zualkernan Sajal K. Das Big Data Energy Management, Analytics and Visualization for Residential Areas
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
author	Ragini Gupta A. R. Al-Ali Imran A. Zualkernan Sajal K. Das
author_facet	Ragini Gupta A. R. Al-Ali Imran A. Zualkernan Sajal K. Das
author_sort	Ragini Gupta
title	Big Data Energy Management, Analytics and Visualization for Residential Areas
title_short	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_sort	big data energy management, analytics and visualization for residential areas
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/595e71e9e5ae41cfaa4c07d8f828ec00
work_keys_str_mv	AT raginigupta bigdataenergymanagementanalyticsandvisualizationforresidentialareas AT aralali bigdataenergymanagementanalyticsandvisualizationforresidentialareas AT imranazualkernan bigdataenergymanagementanalyticsandvisualizationforresidentialareas AT sajalkdas bigdataenergymanagementanalyticsandvisualizationforresidentialareas
_version_	1718420610044919808

Big Data Energy Management, Analytics and Visualization for Residential Areas

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