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University of Suceava
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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  1/2021 - 11
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Generating Manageable Electricity Demand Capacity for Residential Demand Response Studies by Activity-based Load Models

SONMEZ, M. A. See more information about SONMEZ, M. A. on SCOPUS See more information about SONMEZ, M. A. on IEEExplore See more information about SONMEZ, M. A. on Web of Science, BAGRIYANIK, M. See more information about BAGRIYANIK, M. on SCOPUS See more information about BAGRIYANIK, M. on SCOPUS See more information about BAGRIYANIK, M. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,828 KB) | Citation | Downloads: 12 | Views: 12

Author keywords
consumer behavior, load management, power demand, power distribution, smart grids

References keywords
energy(21), buildings(13), jenbuild(9), domestic(9), electricity(8), modeling(7), model(6), demand(5), consumption(5), building(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2021-02-28
Volume 21, Issue 1, Year 2021, On page(s): 99 - 108
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2021.01011
Web of Science Accession Number: 000624018800011
SCOPUS ID: 85102815185

Abstract
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Full text preview
Manageable electricity demand capacity and the user activities that make up this demand is crucial for aggregators in residential demand response events. In this study, it was aimed to generate residential electricity power profiles by the enhanced activity-based load models to determine manageable demand potential. A novel method that aggregators may estimate realistic residential manageable demand capacity was presented. The method can also be used to specify which incentives that cause suitable activity changes of the consumer. Studies were performed on several home appliances associated with different activities. Using load models that are based on collected energy consumption data, consumer behaviors, behavioral adaptations, habits, and physical determinants were embedded in both activities and loads' power profiles. It was observed from simulations that deferrable loads had a significant share in total electricity consumption.


References | Cited By  «-- Click to see who has cited this paper

[1] H. Wang, G. Henri, T. Chin-Woo, and R. Rajagopal, "Activity detection and modeling using smart meter data: concept and case studies," IEEE Power & Energy Society General Meeting, August 2020

[2] T. Liu, X. Ding, and N. Gu, "A generic energy disaggregation approach: What and when electrical appliances are used," in 2015 IEEE International Conference on Data Mining Workshop (ICDMW), Nov. 2015, pp. 389-397,
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[CrossRef]


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References Weight

Web of Science® Citations for all references: 4,177 TCR
SCOPUS® Citations for all references: 5,053 TCR

Web of Science® Average Citations per reference: 144 ACR
SCOPUS® Average Citations per reference: 174 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-04-15 14:28 in 141 seconds.




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