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Title: A Method for Modeling Household Occupant Behavior to Simulate Residential Energy Consumption

This paper presents a statistical method for modeling the behavior of household occupants to estimate residential energy consumption. Using data gathered by the U.S. Census Bureau in the American Time Use Survey (ATUS), actions carried out by survey respondents are categorized into ten distinct activities. These activities are defined to correspond to the major energy consuming loads commonly found within the residential sector. Next, time varying minute resolution Markov chain based statistical models of different occupant types are developed. Using these behavioral models, individual occupants are simulated to show how an occupant interacts with the major residential energy consuming loads throughout the day. From these simulations, the minimum number of occupants, and consequently the minimum number of multiple occupant households, needing to be simulated to produce a statistically accurate representation of aggregate residential behavior can be determined. Finally, future work will involve the use of these occupant models along side residential load models to produce a high-resolution energy consumption profile and estimate the potential for demand response from residential loads.
 [1] ;  [1] ;  [1] ;  [1] ;  [2]
  1. ORNL
  2. University of Tennessee, Knoxville (UTK)
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Conference: Innovative Smart Grid Technologies 2014, Washington DC, DC, USA, 20140219, 20140222
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Building Technologies Research and Integration Center (BTRIC)
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
Country of Publication:
United States
American Time Use Survey; Markov chain; Occupant behavior modeling; Residential energy consumption