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Decentralized mechanical ventilation systems had a sustainable growth in the past ten years in order to guarantee air exchange in highly airtight renovated residential buildings. Smart control strategies could provide a solution to the tradeoff between energy efficiency, hygrothermal comfort and indoor air quality. In this paper, a novel fuzzy demand controlled ventilation strategy is proposed, using state-of-the-art sensors often available in ventilation systems. The proposed controller is tested using a co-simulation approach and is compared to other typical mechanical ventilation control strategies. Results show that this approach provides around 25% energy savings in comparison to a constant airflow strategy, and around 12% against typical smart ventilation systems. The obtained differences regarding hygrothermal comfort and indoor air quality are negligible, and the proposed controller succeeds on keeping the indoor environment in the acceptable range. Finally, the developed fuzzy controller could potentially allow control systems to adapt to different occupant preferences, by tailoring the membership functions to each user.

Mechanical ventilation systems acquired relevance in the past decades as a solution to guarantee air exchange in residential energy retrofit. The individualization of the control strategies to the user preferences could enhance the acceptance of innovative technologies and meet the energy efficiency targets for buildings. In this publication, a novel occupant-centered self-learning control strategy for decentralized ventilation systems is introduced. The controller has a standard-based default control profile, which learns the occupants’ preferences using a classification algorithm. The proposed controller is first simulated and compared to other available mechanical ventilation control strategies. A stochastic model for the manual operation of ventilation systems based on artificial comfort profiles is implemented in the simulation model. Results show that the proposed controller improves the indoor environmental conditions (measured with the relative humidity and CO2 concentration) without disregarding energy efficiency. The self-learning controller can successfully adapt itself to these profiles. The proposed solution was implemented in a real apartment. Measurements were carried out for three months, where two occupants operated the ventilation devices. The installed ventilation concept succeeded in maintaining an acceptable indoor environment, while the distinctive user profiles were learned in each room. The bedrooms were shifted towards lower air exchange rates, while in the rest of the rooms higher air exchange rates were preferred. This implementation confirms the individualization potential of this controller. Finally, the novel self-learning strategy provides an adaptive solution for residential decentralized ventilation controllers, targeting the occupant preferences regarding comfort, health and energy efficiency.

Decentralized regenerative mechanical ventilation systems have acquired relevance in recent years for the retrofit of residential buildings. While manufacturers report heat recovery efficiencies over 90%, research has shown that the efficiencies often vary between 60% and 80%. In order to better understand this mismatch, a test facility is designed and constructed for the experimental characterization and validation of regenerative heat exchanger simulation models. A ceramic honeycomb heat exchanger, typical for decentralized regenerative ventilation devices, is measured in this test facility. The experimental data are used to validate two modeling approaches: a one-dimensional model in Modelica and a computational fluid dynamics (CFD) model built in COMSOL Multiphysics®. The results show an overall acceptable thermal performance of both models, the 1D model having a much lower simulation time and, thus, being suitable for integration in building performance simulations. A test case is designed, where the importance of an appropriate thermal and hydraulic modeling of decentralized ventilation systems is investigated. Therefore, the device is integrated into a multizone building simulation case. The results show that including component-based heat recovery and fan modeling leads to 30% higher heat losses due to ventilation and 10% more fan energy consumption than when assuming constant air exchange rates with ideal heat recovery. These findings contribute to a better understanding of the behavior of a growing technology such as decentralized ventilation and confirm the need for further research on these systems.

Mechanical ventilation systems have acquired relevance in the past years in order to guarantee the hygrothermal comfort and indoor air quality (IAQ) in highly retrofitted residential buildings. The optimization of control strategies could provide a solution to this existing trade-off between energy efficiency, hygrothermal comfort and IAQ. In this publication, we propose a co-simulation approach (using EnergyPlus and Modelica) and a mathematical approximation of the discomfort of the occupant (namely, quadratic for relative humidity and exponential for CO2), and apply them to a demand controlled ventilation (DCV) scheme. Results show that this approach provides around 10% energy savings, while improving the thermal comfort, without compromising the humidity comfort or the IAQ. Finally, the developed functions could allow the control schemes to adapt to different occupant preferences, showing potential for future work.