Development of a probabilistic behavioural model creating diverse A/C operation patterns of households

Bongchan Jeong, Jungsoo Kim, Dong Chen and Richard de Dear

Energy, 2023, vol. 263, issue PB

Abstract: Studies that attempt to model real patterns of residential occupant behaviour have become increasingly popular over the last decade. Most research of this kind tend to produce behaviour profiles based on an average occupant, yet the prediction of residential energy consumption varies significantly according to behavioural profiles of individual households. Oftentimes, averaged behavioural profiles are inadequate for predicting energy demands accurately, therefore requiring models to incorporate the diversity of occupants’ behaviours. The main objective was to develop an air-conditioning (A/C) usage model that produces diverse A/C cooling and heating operation patterns in residential settings. Longitudinal field observations were conducted in 41 Brisbane region households of Australia over a one-year period. Regression coefficients in the model were set to be drawn from a normal distribution to reproduce observed variability in A/C operation patterns amongst the households. Simulations were performed on a validation dataset to test the predictive skill of the model. The result indicated that the frequency and duration of A/C usage observed in the validation dataset well matched with the prediction made by the model. The proposed model could be incorporated in building energy simulation tools to predict realistic energy use for A/C in a given residential building design.

Keywords: Air conditioning; Building energy performance simulation; Occupant behaviour; Occupant diversity; Residential (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054422202566X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:263:y:2023:i:pb:s036054422202566x

DOI: 10.1016/j.energy.2022.125680

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().