Abstract/Summary

Residential activity modelling has attracted considerable attention over the last years. This is particularly due tothe 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. Thispaper details the implementation of a new methodology for the analysis of empirical activity data that allows forthe identification of characteristic behavioural patterns within them. The identified patterns are then used as thebasis for the construction of a high-resolution residential user activity model. The model attempts to capture thestatistical 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. Inaddition to the modelling approach, a new metric for assessing the quality of activity sequences simulations isproposed. Given the amount of empirical data contained in any of the individual time-use datasets currentlyavailable, it would appear that the performance improvement over the predominant first-order Markov chainapproach is modest. However, the validation results show that the proposed approach has the potential forbroadening our understanding of the scheduling of activities in people’s day-to-day lives and how this relates tothe observed variability in both activity and energy consumption patterns.