Towards performance-based approaches for smart residential ventilation: a robust methodology for ranking the systems and decision-making

Smart ventilation which provides air renewal thanks to its variable airflows adjusted on the needs can improve both indoor air quality (IAQ) and energy performance of buildings. However, such performance gains should be quantified with performance-based approaches. In this paper, we propose to extend the performance-based approach with a robust methodology to rank the ventilation systems performance. Such a methodology could be used in a decision-making tool at the design stage of buildings. Indeed, when simulations are carried out, we generally obtain a relative range of the theoretical performances, which should be achieved for each tested ventilation strategy. Nevertheless, it does not allow to rank the ventilation systems performances and to choose the most relevant one from an overall performance point-of-view. In this work the overall performance aspect was focused on IAQ and energy performance through five IAQ - and one energy - performance indicator.
We propose in this paper a simplified approach in 3 keys steps (Figure 1) adapted from existing robust assessment methods, to achieve a robust ranking of the systems based on the aggregation of performance indicators results using Simple Additive Method (SAW). In the present work, five ventilation systems have been tested with several sets of input parameters (500 simulations). In addition, three reference scenarios for input values (low, reference, high) were used for robustness assessment. We compared the ranking calculated with 500 simulations with the ranking calculated with three reference scenarios. The objective was to assess whether the three reference scenarios are sufficient to obtain a relevant ranking of ventilation systems or if more simulations are needed to achieve this goal.
Our results showed that the aggregation of the performance indicators with the SAW method is relatively accurate compared to the performance observed individually by each indicator. Then, the calculation of the design score with the minimax regret robustness method offers a clear advantage to highlight the difference between the ventilation systems, to rank them by including the uncertainty of several simulations. In addition, we show that the use of the three reference scenarios could be sufficient to obtain a relevant ranking of the ventilation systems, in comparison with 500 simulations. However, if the number of simulations is limited, we propose to perform in priority the reference scenario, for an “optimistic performance ranking”, or the reference high scenario for a “conservative performance ranking”. Nevertheless, if there are no constraint, we encourage the decision maker to simulate at least the three reference scenarios and ideally 500 scenarios or more. The latter reinforces the validity of the calculated design score and ranking by including the uncertainty on input parameters.