Occupancy Sensing and Prediction for Automated Energy Savings

The ability to sense and predict occupancy – i.e. to establish when the residents are and will be in a building – represents a basic requirement for the energy-efficient operation of many building automation systems. In residential households, in particular, the absence of all residents allows a heating controller to automatically lower the temperature of the home, thereby saving energy that would have been otherwise wasted on heating an empty building. However, if the home has been thus allowed to cool, a boiler and heat distribution system need a non-negligible time to reheat the home to a comfortable temperature. Therefore, to avoid a loss of comfort, a heating control system also requires a sufficiently accurate prediction of when the occupants are going to return in order to trigger the heating at the right time. Since space heating accounts for a large fraction of residential energy use (e.g. 68% in the European Union member states), heating control systems based on occupancy sensing and prediction – often referred to as smart thermostats – play an important role in reducing energy consumption and carbon dioxide emissions, while at the same time ensuring occupant comfort. The objective of this thesis is thus to investigate how the two main computational components of a smart thermostat – occupancy sensing, based on sensors that typically exist in a residential environment, as well as occupancy prediction from historical occupancy patterns – can be used to automatically reduce the energy consumption of a heating system while trying to maximise thermal comfort. Current smart thermostats require the installation of dedicated hardware to sense whether the occupants are at home or away. This increases installation and maintenance costs and thus prevents widespread adoption of such potentially energy-saving solutions. To overcome this hurdle, we investigate the suitability of opportunistically using devices already existing in households to sense occupancy. This opportunistic sensing approach seeks to utilise available devices to replace or augment dedicated infrastructures. An example are smart electricity meters, which are mandated to be installed in many households worldwide. We hypothesise that the information contained in the electrical load of the