Buildings are amongst the largest consumers of electrical energy in developed countries. Building efficiency can be improved by adapting building systems to a change in the environment or user context. Appropriate action, however, can only be taken if the building control system has access to reliable real-time data. Sensors providing this data need to be ubiquitous, accurate, have low maintenance cost, and should not violate privacy of building occupants.
Optimal control of Heating, Ventilation, and Air Conditioning (HVAC) is an important step towards reducing car- bon footprint of buildings and requires a balance between the en- ergy reductions and occupant comfort. Conventional thermostats for temperature set points provide a single point of user input, often leading to significant thermal discomfort for occupants. We propose instead to include users in the HVAC control loop through smart-phone based votes about their thermal comfort for aggregated control of HVAC. Unlike existing approaches that require in-situ sensors or build complex comfort models of individual users, we propose a model- and sensor-free HVAC control algorithm that uses simple user input (hot/cold) and adapts to changing office occupancy or ambient temperature in real time. We develop an iterative data fusion algorithm that finds optimal temperature in offices with multiple users and propose techniques that can aggressively save energy by drifting indoor temperatures towards the outdoor temperature.