The chapter reviews the impact of occupancy in buildings, in particular in thermal comfort and energy efficiency. Concerning the first issue, this chapter will first propose a means to estimate occupancy, the impact of occupation in thermal comfort measured by the Predicted Mean Vote index, and its use for real-time control of HVAC equipment. All data used are measured data of a real university building under normal occupation. The effect of occupancy in energy efficiency will focus on the residential segment, using data of a recent installation of a data acquisition and control system in a household located in the south of Portugal. This work shows that the impact of occupancy in electricity consumption becomes more evident as the electric energy is being desegregated and that the availability of this information by the occupants can be used to improve energy efficiency. Moreover, the use of occupation in the design of electric consumption forecasting methods will also be discussed.
Keywords: Artificial neural networks, Computational learning, Data acquisition, Energy efficiency, Electricity consumption, Forecasting models, HVAC, Multiobjective genetic algorithms, Occupation, Thermal comfort.