Residential activity pattern modelling through stochastic chains of variable memory length

José Luis Ramírez-Mendiola, Philipp Grünewald and Nick Eyre

Applied Energy, 2019, vol. 237, issue C, 417-430

Abstract: Residential activity modelling has attracted considerable attention over the last years. This is particularly due to the fact that residential energy demand loads are highly dependent on the activity patterns of the household. Therefore, activity models are being increasingly used to underpin high-resolution energy demand models. This paper details the implementation of a new methodology for the analysis of empirical activity data that allows for the identification of characteristic behavioural patterns within them. The identified patterns are then used as the basis for the construction of a high-resolution residential user activity model. The model attempts to capture the statistical characteristics of the empirical data in the form of a stochastic process with memory of variable length. The proposed model is compared to a model based on the predominant first-order Markov chain approach. In addition to the modelling approach, a new metric for assessing the quality of activity sequences simulations is proposed. Given the amount of empirical data contained in any of the individual time-use datasets currently available, it would appear that the performance improvement over the predominant first-order Markov chain approach is modest. However, the validation results show that the proposed approach has the potential for broadening our understanding of the scheduling of activities in people’s day-to-day lives and how this relates to the observed variability in both activity and energy consumption patterns.

Keywords: Activity modelling; Residential activity patterns; Time-use; Stochastic modelling; Energy demand modelling (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261919300194
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:appene:v:237:y:2019:i:c:p:417-430

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2019.01.019

Access Statistics for this article

Applied Energy is currently edited by J. Yan

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