Electricity Usage Scheduling in Smart Building Environments Using Smart Devices

With the recent advances in smart grid technologies as well as the increasing dissemination of smart meters, the electricity usage of every moment can be detected in modern smart building environments. Thus, the utility company adopts different price of electricity at each time slot considering the...

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Main Authors: Eunji Lee, Hyokyung Bahn
Format: Article
Language:English
Published: Hindawi Limited 2013-01-01
Series:The Scientific World Journal
Online Access:http://dx.doi.org/10.1155/2013/468097
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spelling doaj-c60ef0da1fcc4cf3aea9e304883203c32020-11-24T21:45:15ZengHindawi LimitedThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/468097468097Electricity Usage Scheduling in Smart Building Environments Using Smart DevicesEunji Lee0Hyokyung Bahn1Department of EECS, University of Michigan, Ann Arbor, MI 48105, USADepartment of Computer Engineering, Global Top 5 Research Institute, Ewha University, Seoul 120-750, Republic of KoreaWith the recent advances in smart grid technologies as well as the increasing dissemination of smart meters, the electricity usage of every moment can be detected in modern smart building environments. Thus, the utility company adopts different price of electricity at each time slot considering the peak time. This paper presents a new electricity usage scheduling algorithm for smart buildings that adopts real-time pricing of electricity. The proposed algorithm detects the change of electricity prices by making use of a smart device and changes the power mode of each electric device dynamically. Specifically, we formulate the electricity usage scheduling problem as a real-time task scheduling problem and show that it is a complex search problem that has an exponential time complexity. An efficient heuristic based on genetic algorithms is performed on a smart device to cut down the huge searching space and find a reasonable schedule within a feasible time budget. Experimental results with various building conditions show that the proposed algorithm reduces the electricity charge of a smart building by 25.6% on average and up to 33.4%.http://dx.doi.org/10.1155/2013/468097
collection DOAJ
language English
format Article
sources DOAJ
author Eunji Lee
Hyokyung Bahn
spellingShingle Eunji Lee
Hyokyung Bahn
Electricity Usage Scheduling in Smart Building Environments Using Smart Devices
The Scientific World Journal
author_facet Eunji Lee
Hyokyung Bahn
author_sort Eunji Lee
title Electricity Usage Scheduling in Smart Building Environments Using Smart Devices
title_short Electricity Usage Scheduling in Smart Building Environments Using Smart Devices
title_full Electricity Usage Scheduling in Smart Building Environments Using Smart Devices
title_fullStr Electricity Usage Scheduling in Smart Building Environments Using Smart Devices
title_full_unstemmed Electricity Usage Scheduling in Smart Building Environments Using Smart Devices
title_sort electricity usage scheduling in smart building environments using smart devices
publisher Hindawi Limited
series The Scientific World Journal
issn 1537-744X
publishDate 2013-01-01
description With the recent advances in smart grid technologies as well as the increasing dissemination of smart meters, the electricity usage of every moment can be detected in modern smart building environments. Thus, the utility company adopts different price of electricity at each time slot considering the peak time. This paper presents a new electricity usage scheduling algorithm for smart buildings that adopts real-time pricing of electricity. The proposed algorithm detects the change of electricity prices by making use of a smart device and changes the power mode of each electric device dynamically. Specifically, we formulate the electricity usage scheduling problem as a real-time task scheduling problem and show that it is a complex search problem that has an exponential time complexity. An efficient heuristic based on genetic algorithms is performed on a smart device to cut down the huge searching space and find a reasonable schedule within a feasible time budget. Experimental results with various building conditions show that the proposed algorithm reduces the electricity charge of a smart building by 25.6% on average and up to 33.4%.
url http://dx.doi.org/10.1155/2013/468097
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