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Stefan cel Mare
University of Suceava
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Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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 HIGHLY CITED PAPER 

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
 
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Download PDF pdficon (1,828 KB) | Citation | Downloads: 997 | Views: 2,117

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,
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 7]


[3] J. Widen and E. Wackelgard, "A high-resolution stochastic model of domestic activity patterns and electricity demand," Applied Energy, vol. 87, no. 6, pp. 1880-1892, Jun. 2010,
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[4] P. Hoes, J. L. M. Hensen, M. G. L. C. Loomans, B. de Vries, and D. Bourgeois, "User behavior in whole building simulation," Energy and Buildings, vol. 41, no. 3, pp. 295-302, Mar. 2009,
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[CrossRef] [Web of Science Times Cited 255] [SCOPUS Times Cited 298]


[6] T. Hong, D. Yan, S. D'Oca, and C. Chen, "Ten questions concerning occupant behavior in buildings: The big picture," Building and Environment, vol. 114, pp. 518-530, Mar. 2017,
[CrossRef] [Web of Science Times Cited 369] [SCOPUS Times Cited 424]


[7] D. Calì, R. K. Andersen, D. Müller, and B. W. Olesen, "Analysis of occupants' behavior related to the use of windows in German households," Building and Environment, vol. 103, pp. 54-69, Jul. 2016,
[CrossRef] [Web of Science Times Cited 109] [SCOPUS Times Cited 134]


[8] R. Yao and K. Steemers, "A method of formulating energy load profile for domestic buildings in the UK," Energy and Buildings, vol. 37, no. 6, pp. 663-671, Jun. 2005,
[CrossRef] [Web of Science Times Cited 370] [SCOPUS Times Cited 430]


[9] O. Motlagh, P. Paevere, T. S. Hong, and G. Grozev, "Analysis of household electricity consumption behaviours: Impact of domestic electricity generation," Applied Mathematics and Computation, vol. 270, pp. 165-178, Nov. 2015,
[CrossRef] [Web of Science Times Cited 34] [SCOPUS Times Cited 40]


[10] C. Oberst and R. Madlener, "Prosumer Preferences Regarding the Adoption of Micro-Generation Technologies: Empirical Evidence for German Homeowners," Social Science Research Network, Rochester, NY, SSRN Scholarly Paper ID 2670035, Sep. 2015.
[CrossRef]


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[CrossRef] [Web of Science Times Cited 125] [SCOPUS Times Cited 165]


[12] F. McLoughlin, A. Duffy, and M. Conlon, "Characterising domestic electricity consumption patterns by dwelling and occupant socio-economic variables: An Irish case study," Energy and Buildings, vol. 48, pp. 240-248, May 2012,
[CrossRef] [Web of Science Times Cited 293] [SCOPUS Times Cited 358]


[13] H. A. Aalami, M. P. Moghaddam, and G. R. Yousefi, "Modeling and prioritizing demand response programs in power markets," Electric Power Systems Research, vol. 80, no. 4, pp. 426-435, Apr. 2010,
[CrossRef] [Web of Science Times Cited 350] [SCOPUS Times Cited 426]


[14] L. G. Swan and V. I. Ugursal, "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, vol. 13, no. 8, pp. 1819-1835, Oct. 2009,
[CrossRef] [Web of Science Times Cited 1297] [SCOPUS Times Cited 1547]


[15] A. Capasso, W. Grattieri, R. Lamedica, and A. Prudenzi, "A bottom-up approach to residential load modeling," IEEE Transactions on Power Systems, vol. 9, no. 2, pp. 957-964, May 1994,
[CrossRef] [Web of Science Times Cited 334] [SCOPUS Times Cited 416]


[16] J. V. Paatero and P. D. Lund, "A model for generating household electricity load profiles," International Journal of Energy Research, vol. 30, no. 5, pp. 273-290, 2006,
[CrossRef] [Web of Science Times Cited 341] [SCOPUS Times Cited 424]


[17] M. Stokes, "Removing barriers to embedded generation: a fine-grained load model to support low voltage network performance analysis," 2005, Accessed: Sep. 06, 2020. [Online]. Available: https://dora.dmu.ac.uk/handle/2086/4134

[18] S. Firth, K. Lomas, A. Wright, and R. Wall, "Identifying trends in the use of domestic appliances from household electricity consumption measurements," Energy and Buildings, vol. 40, no. 5, pp. 926-936, Jan. 2008,
[CrossRef] [Web of Science Times Cited 211] [SCOPUS Times Cited 259]


[19] T. Zhang, P.-O. Siebers, and U. Aickelin, "Modelling electricity consumption in office buildings: An agent based approach," Energy and Buildings, vol. 43, no. 10, pp. 2882-2892, Oct. 2011,
[CrossRef] [Web of Science Times Cited 75] [SCOPUS Times Cited 94]


[20] E. de Groot, M. Spiekman, and I. Opstelten, "361: Dutch Research into User Behaviour in Relation to Energy Use of Residences," p. 5, 2008

[21] Y. S. Lee and A. M. Malkawi, "Simulating multiple occupant behaviors in buildings: An agent-based modeling approach," Energy and Buildings, vol. 69, pp. 407-416, Feb. 2014,
[CrossRef] [Web of Science Times Cited 143] [SCOPUS Times Cited 164]


[22] I. Richardson, M. Thomson, and D. Infield, "A high-resolution domestic building occupancy model for energy demand simulations," Energy and Buildings, vol. 40, no. 8, pp. 1560-1566, Jan. 2008,
[CrossRef] [Web of Science Times Cited 396] [SCOPUS Times Cited 475]


[23] I. Richardson, M. Thomson, D. Infield, and A. Delahunty, "Domestic lighting: A high-resolution energy demand model," Energy and Buildings, vol. 41, no. 7, pp. 781-789, Jul. 2009,
[CrossRef] [Web of Science Times Cited 143] [SCOPUS Times Cited 161]


[24] M. Stokes, M. Rylatt, and K. Lomas, "A simple model of domestic lighting demand," Energy and Buildings, vol. 36, no. 2, pp. 103-116, Feb. 2004,
[CrossRef] [Web of Science Times Cited 94] [SCOPUS Times Cited 114]


[25] L. Bottaccioli, S. Di Cataldo, A. Acquaviva, and E. Patti, "Realistic Multi-Scale Modeling of Household Electricity Behaviors," IEEE Access, vol. 7, pp. 2467-2489, 2019,
[CrossRef] [Web of Science Times Cited 29] [SCOPUS Times Cited 32]


[26] ***, KONDA "Report on social gender in Turkey: The Life-Styles Survey," (in Turkish), 2018

[27] R. Stamminger et al., "Synergy potential of smart appliances," Nov. 2008

[28] D. S. Parker, P. Fairey, and J. D. Lutz, "Estimating daily domestic hot-water use in North American Homes," ASHRAE Transactions, vol. 121, no. 2, pp. 258-271, Jul. 2015



References Weight

Web of Science® Citations for all references: 5,770 TCR
SCOPUS® Citations for all references: 6,935 TCR

Web of Science® Average Citations per reference: 199 ACR
SCOPUS® Average Citations per reference: 239 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 2024-11-13 21:21 in 162 seconds.




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