Publication:
A Big Data Analytics Model for Household Electricity Consumption Tracking and Monitoring

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dc.citedby3
dc.contributor.authorDollah R.en_US
dc.contributor.authorAris H.en_US
dc.contributor.authorid57202188451en_US
dc.contributor.authorid13608397500en_US
dc.date.accessioned2023-05-29T07:27:26Z
dc.date.available2023-05-29T07:27:26Z
dc.date.issued2019
dc.descriptionBig data; Data Analytics; Electric power utilization; Energy utilization; Learning systems; Object oriented programming; BDA framework; Billing systems; Consumption patterns; descriptive analytics; Electricity-consumption; Evaluation results; Framework development; Household electricity consumption; Predictive analyticsen_US
dc.description.abstractThe abundance of data nowadays can offer infinite opportunities and possibilities if being systematically explored. Exploration of the data can be achieved through the application of big data analytics (BDA). Consequently, a number of BDA models are seen developed in a number of sectors. Energy is one of the sectors that can potentially benefit from the BDA initative. Consumers' energy related data that come from sources such as smart meters and billing systems are good candidates for the data. Through the application of the BDA on consumers' data, useful information such as consumption pattern and trend can be obtained. Studies showed that awareness on the energy consumption is able to contribute up to 20% saving in its use. Furthermore, BDA models in energy sector, particularly on electricity that address the consumers side of the sector are still lacking. Therefore, in this research, a BDA model for household electricity consumption tracking and monitoring was developed based on the common BDA models' layers. Using the descriptive and predictive analytics to analyse the big data amassed from the consumers, the model provides the required information and prediction that enables the consumers to view, track, compare and plan their electricity consumption at home. Evaluation results showed that the model is deemed applicable and able to attain its objective. Through the proposed BDA model, consumers can be better guided in managing their electricity consumption. � 2018 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8629769
dc.identifier.doi10.1109/ICBDAA.2018.8629769
dc.identifier.epage49
dc.identifier.scopus2-s2.0-85062777455
dc.identifier.spage44
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85062777455&doi=10.1109%2fICBDAA.2018.8629769&partnerID=40&md5=abbf20d66e1c72579cc9a5c843c6f3e5
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24814
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2018 IEEE Conference on Big Data and Analytics, ICBDA 2018
dc.titleA Big Data Analytics Model for Household Electricity Consumption Tracking and Monitoringen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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