The pattern of electricity use in an individual domestic dwelling is highly dependent upon the activities of the occupants and their associated use of electrical appliances. This paper presents a high-resolution model of domestic electricity use, that is based upon a combination of patterns of active occupancy (i.e. when people are at home and awake), and daily activity profiles that characterise how people spend their time performing certain activities. One-minute resolution synthetic electricity demand data is created through the simulation of appliance use; the model covers all major appliances commonly found in the domestic environment. In order to validate the model, electricity demand was recorded over the period of a year within 22 dwellings in the East Midlands, UK. A thorough quantitative comparison is made between the synthetic and measured data sets, showing them to have similar statistical characteristics. A freely downloadable example of the model is made available and may be configured to the particular requirements of users or incorporated into other models.

Citation: RICHARDSON, I., THOMSON, M. and INFIELD, D., 2008. A high-resolution domestic building occupancy model for energy demand simulations. Energy and Buildings, 40 (8), pp.1560-1566 Additional Information:• This article was published in the journal, Energy and Build- AbstractEnergy use in the home is a major source of carbon emissions and is highly dependent on the activities of the residents. More specifically, the timing of energy use, particularly electricity, is highly dependent on the timing of the occupants' activities. Thus, in order to model domestic demand profiles with high temporal resolution, for example in the context of designing and assessing demand side management systems (including the time-shifting of demand), it is of great benefit to take account of residents' behaviour in terms of when they are likely to be using household appliances, lighting and heating. This paper presents a thorough and detailed method for generating realistic occupancy data for UK households, based upon surveyed time-use data describing what people do and when. The approach presented generates statistical occupancy time-series data at a ten-minute resolution and takes account of differences between weekdays and weekends. The model also indicates the number of occupants that are active within a house at a given time, which is important for example in order to model the sharing of energy use (shared use of appliances etc.) The data from the model can be used as input to any domestic energy model that uses occupancy time-series as a base variable, or any other application that requires detailed occupancy data. The model has been implemented in Excel and is available for free download.

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