Normalising domestic space heating demand using post hoc models

Current evidence suggests that the energy performance gap between predicted and actual use of energy in buildings is significantly weighted towards under prediction and can be as high as 200%. High-quality modelled and actual data are needed to ensure like for like comparisons when investigating the energy performance gap. Internal temperature (ti) normalisation is a key process to ensure like for like comparisons but is often hampered by the lack of the original model due to the time lag between design, construction and occupancy. Here, we demonstrate the use of models created after data collection – i.e. post hoc – as a substitute for original models in evaluating the energy performance gap. The robustness of the internal temperature normalisation factor (fti) is tested using measured data from 20 Passivhaus homes. The data from each home are inputted into 10 Passive House Planning Package and 10 Standard Assessment Procedure models with highly different domestic and non-domestic building configurations, creating 400 model variants. Each variant is further split into four cases of varying internal gains and solar radiation creating a total of 1600 variants. Results demonstrate that fti is resilient to differences in building configuration, solar radiation levels and varying internal gains (SEM？<？0.02). Even though SEM increases when measured internal temperatures are below base assumptions, the impact of this error on the computed space heating demand is at most 4%. This suggests that post hoc models can be a substitute for actual models in evaluating the energy performance gap and that limited site data can still yield robust results. Practical application: Identifying the causes of the energy performance gap (the difference between modelled and measure energy demand) is complex. Normalising space heating demand for internal temperatures means that some differences between modelled and actual space heating demand can be accounted for. Building models such as Passive House Planning Package (PHPP) and Standard Assessment Procedure (SAP) are readily available and allow variations in climate and temperature data to be inputted. This research demonstrates that in practice any PHPP and SAP model can be used for normalisation, not just one that is building specific and that some parameters (internal temperature) are more important than others. This provides a simple and easily accessible approach to temperature normalisation that can be applied by industry to domestic dwellings