Nambi, SNAU, Pournaras, E and Venkatesha Prasad, R (2016) Temporal Self-Regulation of Energy Demand. IEEE Transactions on Industrial Informatics, 12 (3). pp. 1196-1205. ISSN 1551-3203
Abstract
The increase in the deployment of smart meters has enabled collection of fine-grained energy consumption data at consumer premises. Analysis of this real-time energy consumption data bestows new opportunities for better demand-response (DR) programs. This paper offers a new perspective to study energy demand and helps in designing novel mechanisms for decentralized demand-side management. Specifically, a new concept of finding the demand states using energy consumption of consumers over time and feasible transitions therein is introduced. It is shown that the orchestration of temporal transitions between the demand states can meet broad range of smart grid objectives. An online demand regulation model is developed that captures the temporal dynamics of energy demand to identify target consumers for different DR programs. This methodology is empirically evaluated and validated using data from more than 4000 households, which were part of a real-world smart grid project. This paper is the first one to comprehensively analyze the temporal dynamics of demands.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016, IEEE. This is an author produced version of a paper published in IEEE Transactions on Industrial Informatics. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Time series analysis; Gold; Time measurement; smart grids; Computational modeling; smart meters |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Feb 2020 11:58 |
Last Modified: | 19 Feb 2020 11:58 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/tii.2016.2554519 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:157104 |