This article introduces a methodological approach for analysing time series data from multiple sensors in order to estimate home occupancy. The approach combines the Dempster-Shafer theory, which allows the fusion of ‘evidence’ from multiple sensors, with the Hidden Markov Model. The procedure addresses some of the practicalities of occupancy estimation including the blind estimation of sensor distributions during unoccupied and occupied states, and issues of occupancy inference when some sensors have missing data. The approach is applied to preliminary data from a residential family home on the North Coast of Scotland giving an occupancy profile, which is validated with information provided by the household. Knowledge of occupancy is fused with consumption behaviour and simple metric developed to allow the assessment of how likely it is that a household can participate in demand response at different periods during the day. The benefits of demand response initiatives are qualitatively discussed.
|Number of pages||13|
|Journal||Energy and Buildings|
|Early online date||30 Apr 2016|
|Publication status||Published - 1 Aug 2016|
- Occupancy Sensing
- Demand response
- Home automation
- Energy efficiency
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Edward Hugh Owens
- School of Energy, Geoscience, Infrastructure and Society - Professor
- School of Energy, Geoscience, Infrastructure and Society, Institute for Sustainable Building Design - Professor
- School of Energy, Geoscience, Infrastructure and Society, Institute for Infrastructure & Environment - Professor
Person: Academic (Research & Teaching)