PredicTAIL, a prediction method for indoor environmental quality in buildings undergoing deep energy renovation based on the TAIL rating scheme

The recently developed TAIL rating scheme enables assessment of the changes in the indoor environmen- tal quality (IEQ) associated with a building’s deep energy renovation (DER) and classification of the resulting quality levels of the thermal (T), acoustic (A), and luminous (visual) (L) environments and indoor air quality (I). Since the TAIL rating is primarily based on measurements, it cannot be determined prior to renovation operations to help design the IEQ. To fill this gap, the PredicTAIL method was devel- oped in the present study to predict the changes in ten of the twelve TAIL parameters as a result of DER. These parameters are indoor air temperature, relative humidity, sound pressure level, daylight factor, illuminance, and concentrations of carbon dioxide, formaldehyde, benzene, radon, and PM2.5; no predic- tion is made for ventilation rate or mold. To examine the feasibility of the PredicTAIL method and the sen- sitivity of the existing models for quantifying changes in the TAIL parameters corresponding to different renovation strategies, simulations were performed in a hotel and an office building using TRNSYS, IDA ICE, ACOUBAT, MATHIS-QAI, and PHANIE. These modeling tools were first benchmarked against the TAIL parameters measured in the buildings before renovation. Once the agreement between measure- ments and modeling was considered acceptable, four pragmatic renovation scenarios were applied, and their impact on the IEQ parameters was quantitatively modeled. The simulations showed that the quality levels of the IEQ parameters were improved or unchanged for some parameters but degraded for other parameters after DER. The changes in the IEQ parameters and the TAIL rating depended on the renovation scenarios, suggesting that the PredicTAIL method is sufficiently sensitive to guide renova- tion design