HVAC systems are responsible for a significant part of the total building energy consumption and in many cases a large portion of that energy is actually used inefficiently. The ability of these systems to adapt to changing numbers of occupants will play a major role in reducing their energy load. Demand Controlled Ventilation systems are helping towards that goal and many of them use CO2 sensors as their core information source, whereby the system tries to maintain CO2 near or under a specified set point. Besides providing a simple feedback mechanism, CO2 can also be utilised in predictive models for indoor air quality and in the estimation of occupancy. The choice of location for the CO2 sensors however can have an impact in the estimations. The objective of this study is two-pronged: Firstly to identify a relationship between occupancy, CO2 setpoint, ventilation rate and cooling load for a university lecture theatre, in a way that it can be used as a decision tool by the facility manager to balance indoor air quality and energy consumption. Secondly, to investigate and quantify how representative the CO2 values detected by different sensors within the lecture theatre are and whether any differences can have a significant effect in estimating the number of occupants within. Results show that both CO2 and occupancy can be estimated with good accuracy with little effect from sensor placement and that it is feasible to easily visualise the balance between CO2 threshold and cooling load for a given level of occupancy. (C) 2018 Elsevier B.V. All rights reserved.