Comparison of prediction tools to determine their reliability on calculating operational heating consumption by monitoring no-fines concrete dwellings

Nowadays most retrofit decisions are based on reducing CO2 / heating consumption. The aim of this study was to determine the reliability of three tools (RdSAP, SAP and IES) often used to predict these reductions. Three no-fines concrete (NFC) dwellings (Cl, C2, and C3) with similar floor area and construction but different occupants were monitored. Key information about the thermal performance of the fabric; the behaviour of the occupants and the energy consumption was collected before and after 110 mm of external wall insulation (EWI) was added. The target was a 30% reduction on energy consumption due to the EWI. However, only C3 decreased it by 30% as expected, C2 only by 14% due to a subtle rebound effect and Cl actually increased consumption by 75%, due to rebound effect. Steady state tools (RdSAP and SAP) were found to be inaccurate in predicting the operational energy consumption of dwellings, only dynamic performance analysis software (IES) was suitable to carry out this type of prediction accurately. However, this type of software requires highly accurate and detailed information regarding: the baseline performance of the fabric, external weather conditions and, most importantly, accurate pre- and post- heating operational habits of the occupants. Few retrofitting projects have the resources and time to gather this information. Unless those are available, the retrofit decisions should be based in a different criteria, rather than using inaccurate SAP or RdSAP energy consumption predictions. The coefficient of heat loss of the fabric of a dwelling is independent of the occupants. SAP was found quick to calculate reasonable predictions of the coefficient, by using accurate fabric data, and to show the impact of different factors on the heat loss of the fabric. Therefore, it could be claimed that the coefficient of heat loss of the fabric is a suitable alternative criteria to make pre-retrofit decisions. (C) 2018 Elsevier B.V. All rights reserved.