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Abstract
Stochastic building occupancy models are increasingly used to underpin building energy demand models, especially those providing high-resolution electricity demand profiles. This paper describes the development of an established two-state active-occupancy model into a four-state model in which the absent/present state and the active/inactive state are treated separately. This provides a distinction between sleeping and absence and so offers an improved basis for demand modelling, particularly high-resolution thermal modelling. The model uses a first-order Markov chain technique and the paper illustrates the value of this approach in duly representing the naturally occurring correlation of occupancy states in multiply occupied dwellings. The paper also describes how the model has been enhanced to avoid under-representation of dwellings with 24h occupancy. The model has been implemented in Excel VBA and made available to download for free. The model is constructed from and verified against UK time-use survey data but could readily be adapted to use similar data from elsewhere.
Highlights
The transition to a low-carbon economy may be expected to require high penetrations of low-carbon technologies such as heat pumps, electric vehicles and photovoltaics [1,2]
Aerts et al adopt a similar technique to develop an occupancy model based on the Belgian time-use survey, stating that compared to the first-order Markov technique “the occurrence of unrealistic occupancy durations is far less probable” with the higher-order Markov technique [25]. Given these criticisms of the first-order Markov chain technique, and the fact that models adopting it have not been validated in terms of their ability to adequately model occupancy state durations, one of the aims of this paper is to quantify the discrepancies in state durations of the model output
One of the aims of the paper is to quantify any discrepancy in state durations between the original time-use survey data and synthetic data generated using the first-order Markov chain technique
Summary
The transition to a low-carbon economy may be expected to require high penetrations of low-carbon technologies such as heat pumps, electric vehicles and photovoltaics [1,2]. The high-resolution model of domestic electricity demand developed by Loughborough University [5,6,7,8] is constructed in this way It uses a two-state active-occupancy model that feeds into determining stochastic switch-on events for individual lighting and domestic appliances. The published model has been used widely within academia and industry for electricity network modelling [9,10,11] It does not, include any detailed representation of thermal demands and, cannot yet be used to properly investigate the effects of the electrification of heating or CHP.
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