•A simple method has been suggested for the estimation of the occupied and unoccupied distributions for different sensors installed in a residential home.•A critical feature of the method is that it does not require extensive recording of ground truth.•Practical occupancy inference through combining Dempster-Shafer’s theory of evidence with Hidden Markov Models has been demonstrated on some preliminary data and appears to be a very reasonable approach.•A methodology has been developed that uses this practical occupancy inference for assessing the possibility of demand response for a particular household at different times of day.•The benefits of occupancy to different demand response initiatives have been qualitatively assessed. 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. Features derived from sensors that monitored electrical power, dew point temperature and indoor CO2 concentration were fused and the Hidden Markov Model applied to predict the occupancy profile. The approach shown is able to predict daytime occupancy, while effectively handling periods of missing sensor data, according to cross-validation with available ground truth information. Knowledge of occupancy is then fused with consumption behaviour and a 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. The approach could be used to assist in the transition towards more active energy citizens, as envisaged by the smart grid.