Abstract
In the EU, buildings account for about 40% of the final energy consumption. Space heating (SH) accounts for 65% of the energy demand in buildings. There is thus a large potential in energy savings, simply by focusing on the reduction of space heating demand. In the Netherlands, there are about 6 million residential buildings constructed before 2005 where thermal performance is far from optimal. All these buildings need to reduce their demand as the first step to meet nZEB requirements. For large scale energy retrofit of buildings, the main challenge is to classify each housing type by the severity of the demand reduction.
A Dutch district in the city of Apeldoorn has been chosen to investigate the energy-saving potential and the robustness of different retrofit packages. Using a bottom-up approach, the energy-saving potential of the existing building types in the mentioned district was analysed by the parametric study of building energy performance simulations in IES VE. Subsequently, the uncertainty in building (energy) performance was assessed using a minimax regret method to compare the robustness of designs. For the covered building types and construction periods, the results suggest that after the upgrade to insulation level from current regulations, implementation of Heat Recovery Ventilation (HRV) reduces the space heating demand by 50–65% while insulation upgrade to passive house standards on yields an average heat demand reduction of only 15%. In contrast, provided with sufficient ventilation, the results suggest that sufficient ventilation eliminates the demand for space cooling in passive building envelopes (excluding climate change effects). In this regard, detached housing is the most and semi-detached is the least prone to the overheated indoor environment. Relying on natural ventilation as the only source of cooling in the summer increases the risk of overheating. Furthermore, upgrading to the nZEB standard building envelope appears to be around 40–50% less economical (€/unit saved energy) than current requirements for building envelope. Lastly, the results show that packages with HRV are about twice as robust as packages without HRV thus highlighting its optimal cost.
A Dutch district in the city of Apeldoorn has been chosen to investigate the energy-saving potential and the robustness of different retrofit packages. Using a bottom-up approach, the energy-saving potential of the existing building types in the mentioned district was analysed by the parametric study of building energy performance simulations in IES VE. Subsequently, the uncertainty in building (energy) performance was assessed using a minimax regret method to compare the robustness of designs. For the covered building types and construction periods, the results suggest that after the upgrade to insulation level from current regulations, implementation of Heat Recovery Ventilation (HRV) reduces the space heating demand by 50–65% while insulation upgrade to passive house standards on yields an average heat demand reduction of only 15%. In contrast, provided with sufficient ventilation, the results suggest that sufficient ventilation eliminates the demand for space cooling in passive building envelopes (excluding climate change effects). In this regard, detached housing is the most and semi-detached is the least prone to the overheated indoor environment. Relying on natural ventilation as the only source of cooling in the summer increases the risk of overheating. Furthermore, upgrading to the nZEB standard building envelope appears to be around 40–50% less economical (€/unit saved energy) than current requirements for building envelope. Lastly, the results show that packages with HRV are about twice as robust as packages without HRV thus highlighting its optimal cost.
Original language | English |
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Article number | 111914 |
Number of pages | 25 |
Journal | Energy and Buildings |
Volume | 260 |
DOIs | |
Publication status | Published - 1 Apr 2022 |
Keywords
- Cost-optimal energy retrofit measures
- Large scale energy retrofit
- Robustness assessment
- Simulation-based optimization