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

Roimah Dollah, Hazleen Aris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The 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.

Original languageEnglish
Title of host publication2018 IEEE Conference on Big Data and Analytics, ICBDA 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages44-49
Number of pages6
ISBN (Electronic)9781538671283
DOIs
Publication statusPublished - 29 Jan 2019
Event2018 IEEE Conference on Big Data and Analytics, ICBDA 2018 - Langkawi, Kedah, Malaysia
Duration: 21 Nov 201822 Nov 2018

Publication series

Name2018 IEEE Conference on Big Data and Analytics, ICBDA 2018

Conference

Conference2018 IEEE Conference on Big Data and Analytics, ICBDA 2018
CountryMalaysia
CityLangkawi, Kedah
Period21/11/1822/11/18

Fingerprint

Electricity
Monitoring
Smart meters
Big data
Electricity consumption
Household
Energy utilization

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems and Management

Cite this

Dollah, R., & Aris, H. (2019). A Big Data Analytics Model for Household Electricity Consumption Tracking and Monitoring. In 2018 IEEE Conference on Big Data and Analytics, ICBDA 2018 (pp. 44-49). [8629769] (2018 IEEE Conference on Big Data and Analytics, ICBDA 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICBDAA.2018.8629769
Dollah, Roimah ; Aris, Hazleen. / A Big Data Analytics Model for Household Electricity Consumption Tracking and Monitoring. 2018 IEEE Conference on Big Data and Analytics, ICBDA 2018. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 44-49 (2018 IEEE Conference on Big Data and Analytics, ICBDA 2018).
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abstract = "The 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.",
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Dollah, R & Aris, H 2019, A Big Data Analytics Model for Household Electricity Consumption Tracking and Monitoring. in 2018 IEEE Conference on Big Data and Analytics, ICBDA 2018., 8629769, 2018 IEEE Conference on Big Data and Analytics, ICBDA 2018, Institute of Electrical and Electronics Engineers Inc., pp. 44-49, 2018 IEEE Conference on Big Data and Analytics, ICBDA 2018, Langkawi, Kedah, Malaysia, 21/11/18. https://doi.org/10.1109/ICBDAA.2018.8629769

A Big Data Analytics Model for Household Electricity Consumption Tracking and Monitoring. / Dollah, Roimah; Aris, Hazleen.

2018 IEEE Conference on Big Data and Analytics, ICBDA 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 44-49 8629769 (2018 IEEE Conference on Big Data and Analytics, ICBDA 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Dollah R, Aris H. A Big Data Analytics Model for Household Electricity Consumption Tracking and Monitoring. In 2018 IEEE Conference on Big Data and Analytics, ICBDA 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 44-49. 8629769. (2018 IEEE Conference on Big Data and Analytics, ICBDA 2018). https://doi.org/10.1109/ICBDAA.2018.8629769