Development of an evaluation methodology to quantify the energy potential of demand controlled ventilation strategies

Demand controlled ventilation (DCV) is seen more and more as a promising way to limit the energy consumption due to ventilation in buildings. However DCV is always a compromise between decreasing the ventilation flow rates and assuring the indoor air quality (IAQ). Ventilation requirements are usually expressed as required air flow rates in the ventilation standards and regulations. Up to now, no consensus for an absolute criterion of IAQ exists in the international scientific community. Quantifying the energy potential of DCV strategies taking into account the indoor air quality is still a big challenge. This paper describes a preliminary study for the development of such an assessment method for DCV strategies in the context of the regulation for the energy performance of buildings in Belgium. This method was based on numerical simulations using the CONTAM software, with a Monte Carlo approach and representative occupation profiles. An important question in this study was the choice of the reference system and/or flow rate used to quantify the energy potential of DCV strategies. Different possible references have been compared and discussed. In the absence of an absolute IAQ criterion and because manual regulation strategies cannot be considered as DCV, we propose to quantify DCV strategies by comparing the DCV strategy to be tested with a system, working at constant flow rate, achieving the same IAQ-level as the tested DCV system. Different DCV strategies have been evaluated using this methodology. The following types of detection have been studied: CO2 or presence detection in the living spaces, and/or relative humidity (RH) detection in the service spaces. The regulation strategies ranged from very complex, with local detection and local regulation in each room independently, to very crude, with central detection and central regulation for the whole dwelling, and a series of intermediary strategies. The main results can be summarized as follows. Applying this methodology to a large range of different DCV strategies, the simulation results lead to reduction factors of 0.61 for a complete DCV system combining CO2 detection in the living spaces and RH detection in the service spaces, 0.64 for the DCV system with only CO2 detection, and around 1 for DCV system with only RH detection in the service spaces. DCV strategies with only RH detection in the service rooms showed a poor energy potential because this kind of detection is not sufficient to control the IAQ in the living spaces. In contrast, DCV strategies with only CO2 detection in the living spaces gave a higher energy potential, depending also on the number of living spaces equipped with a detection sensor.