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Extracting Usage Patterns from Power Usage Data of Homes' Appliances in Smart Home using Big Data Platform

Extracting Usage Patterns from Power Usage Data of Homes' Appliances in Smart Home using Big Data Platform

Ali Reza Honarvar, Ashkan Sami
Copyright: © 2016 |Volume: 11 |Issue: 2 |Pages: 12
ISSN: 1554-1045|EISSN: 1554-1053|EISBN13: 9781466689572|DOI: 10.4018/IJITWE.2016040103
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MLA

Honarvar, Ali Reza, and Ashkan Sami. "Extracting Usage Patterns from Power Usage Data of Homes' Appliances in Smart Home using Big Data Platform." IJITWE vol.11, no.2 2016: pp.39-50. http://doi.org/10.4018/IJITWE.2016040103

APA

Honarvar, A. R. & Sami, A. (2016). Extracting Usage Patterns from Power Usage Data of Homes' Appliances in Smart Home using Big Data Platform. International Journal of Information Technology and Web Engineering (IJITWE), 11(2), 39-50. http://doi.org/10.4018/IJITWE.2016040103

Chicago

Honarvar, Ali Reza, and Ashkan Sami. "Extracting Usage Patterns from Power Usage Data of Homes' Appliances in Smart Home using Big Data Platform," International Journal of Information Technology and Web Engineering (IJITWE) 11, no.2: 39-50. http://doi.org/10.4018/IJITWE.2016040103

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Abstract

Advances in sensing techniques and IOT enabled the possibility to gain precise information about devices in smart home and smart city environments. Data analysis for sensors and devices may help us develop friendlier systems for smart city or smart home. Sequence pattern mining extracts interesting sequence pattern from data. Electricity usage dose follow a sequence of events. In this study the authors investigate this issue and extracted valuable sequence pattern from real appliances' power usage dataset using PrefixSpan. The experiments in this research is implemented on Spark as a novel distributed and parallel big data processing platform on two different clusters and interesting findings are obtained. These findings show the importance of extracting sequence pattern from power usage data to various applications such as decreasing CO2 and greenhouse gas emission by decreasing the electricity usage. The findings also show the needs to bring big data platforms to processing such kind of data which is captured in smart home and smart cities.

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