A MATLAB based occupant driven dynamic model for predicting residential power demand

Brandon J. Johnson, Michael R. Starke, Omar A. Abdelaziz, Roderick K. Jackson, Leon M. Tolbert

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Scopus citations

Abstract

This paper presents a MATLAB based dynamic model for predicting residential power demand. Markov chain based occupant behavior models developed using data gathered by the U.S. Census Bureau in the American Time Use Survey (ATUS) are used in conjunction with models of the most common residential loads to predict residential power demand on a onesecond time scale. First, the methods utilized for the modeling of each residential load are presented. Next, an explanation of how these load models are combined with occupant behavior models to predict residential power demand is given. Simulation results showing the overall contribution of each load to the overall residential sector power demand are shown for both winter and summer cases. Finally, future work will involve the use of this high-resolution dynamic residential model to estimate the potential for demand response from residential loads.

Original languageEnglish
Title of host publicationProceedings of the IEEE Power Engineering Society Transmission and Distribution Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479936557
StatePublished - Jul 23 2014
Event2014 IEEE/PES Transmission and Distribution Conference and Exposition, T and D 2014 - Chicago, United States
Duration: Apr 14 2014Apr 17 2014

Publication series

NameProceedings of the IEEE Power Engineering Society Transmission and Distribution Conference
ISSN (Print)2160-8555
ISSN (Electronic)2160-8563

Conference

Conference2014 IEEE/PES Transmission and Distribution Conference and Exposition, T and D 2014
Country/TerritoryUnited States
CityChicago
Period04/14/1404/17/14

Keywords

  • Dynamic load modeling
  • Markov chain
  • Occupant behavior modeling
  • Residential power demand

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