Improve Home Energy Management System by Extracting Usage Patterns From Power Usage Big Data of Homes' Appliances

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Author(s): Ali Reza Honarvar (Shiraz University, Iran)and Ashkan Sami (Shiraz University, Iran)
Copyright: 2018
Pages: 16
Source title: Handbook of Research on Contemporary Perspectives on Web-Based Systems
Source Author(s)/Editor(s): Atilla Elçi (Hasan Kalyoncu University, Turkey)
DOI: 10.4018/978-1-5225-5384-7.ch007

Keywords: Computer Science & IT / Information Science Reference / Systems and Software Engineering / Web Technologies & Engineering

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Abstract

Many researchers have focused on the reduction of electricity usage in residences because it is a significant contributor of CO2 and greenhouse gases emissions. However, electricity conservation is a tedious task for residential users due to the lack of detailed electricity usage. Home energy management systems (HEMS) are schedulers that schedule and shift demands to improve the energy consumption on behalf of a consumer based on demand response. In this chapter, valuable sequence patterns from real appliances' usage datasets are extracted in peak time and off-peak time of weekdays and weekends to get valuable insight that is applicable in the HEMS. Generated data in smart cities and smart homes are placed in the category of big data. Therefore, to extract valuable information from such data an architecture for the home and city data processing system is proposed, which considers the multi-source smart cities and homes' data and big data processing platforms.

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Improve Home Energy Management System by Extracting Usage Patterns From Power Usage Big Data of Homes' Appliances

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