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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Series: | The Scientific World Journal |
Online Access: | http://dx.doi.org/10.1155/2013/468097 |
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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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AT eunjilee electricityusageschedulinginsmartbuildingenvironmentsusingsmartdevices AT hyokyungbahn electricityusageschedulinginsmartbuildingenvironmentsusingsmartdevices |
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