Energy renovations often result in lower energy savings than expected. Therefore, in this study we investigate nearly 90,000 renovated dwellings in the Netherlands with pre and post renovation data of actual and calculated energy consumption. One of the main additions of this paper, compared to previous studies on thermal renovation, is that it only takes dwellings into account with the same occupants before and after renovation, using a large longitudinal dataset. Overall this paper shows new insights towards the influence of the energy efficiency state of a building prior to energy renovation, the type of building, the number of occupants, the income level of the occupants and the occupancy time on the actual energy savings, the energy saving gap and on the probability of lower energy savings than expected. We also investigate if the influence is different per type of thermal renovation measure. Some of the findings are: it is impossible to conclude which single thermal renovation measure is the most effective because this is dependent on the energy efficiency of the building prior to the energy renovation, type of building, income level and occupancy; occupants with a high income save more energy than occupants with low income; dwellings with employed occupants benefit more from improved building installations than dwellings occupied by unemployed occupants; The prebound and rebound effects are only part of the explanations for lower than expected energy savings; Deep renovations result more often in lower than expected energy savings than single renovation measures but nevertheless they result in the highest average energy saving compared to other thermal renovation measures. The results could be used for more realistic expectations of the energy reduction achieved by thermal renovations, which is important for (amongst others) policy makers, clients and contractors who make use of energy performance contracting, home owners, landlords and (social) housing associations and as a starting point to improve the energy calculation method.
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- Energy saving gap
- Longitudinal data
- Occupant and building characteristics
- Thermal renovations