Performance Gap in Retrofit
Area: Design, planning and building
The performance gap in retrofit refers to the disparity between the predicted and actual energy consumption after a retrofit project, measured in kWh/m2/year. This discrepancy can be substantial, occasionally reaching up to five times the projected energy usage (Traynor, 2019). Sunikka-Blank & Galvin (2012) identify four key factors as contributing to the performance gap: (1) the rebound effect, (2) the prebound effect, (3) interactions of occupants with building components, and (4) the uncertainty of building performance simulation outcomes. Gupta & Gregg (2015) additionally identify elevated building air-permeability rates as a factor leading to imbalanced and insufficient extract flowrates, exacerbating the performance gap. While post occupancy evaluation of EnerPhit—the Passivhaus Institut certification for retrofit—has shown far better building performance in line with predictions, the human impact of building users operating the building inefficiently will always lead to some sort of performance gap (Traynor, 2019, p. 34). Deeper understanding of the prebound effect and the rebound effect can improve energy predictions and aid in policy-making (Galvin & Sunikka-Blank, 2016). Therefore, the ‘prebound effect’ and the ‘rebound effect’, outlined below, are the most widely researched contributors to the energy performance gaps in deep energy retrofit.
Prebound Effect
The prebound effect manifests when the actual energy consumption of a dwelling falls below the levels predicted from energy rating certifications such as energy performance certificates (EPC) or energy performance ratings (EPR). According to Beagon et al. (2018, p.244), the prebound effect typically stems from “occupant self-rationing of energy and increases in homes of inferior energy ratings—the type of homes more likely to be rented.” Studies show that the prebound effect can result in significantly lower energy savings post-retrofit than predicted and designed to achieve (Beagon et al., 2018; Gupta & Gregg, 2015; Sunikka-Blank & Galvin, 2012). Sunikka-Blank & Galvin’s (2012) study compared the calculated space and water heating energy consumption (EPR) with the actual measured consumption of 3,400 German dwellings and corroborated similar findings of the prebound effect in the Netherlands, Belgium, France, and the UK. Noteworthy observations from this research include: (1) substantial variation in space heating energy consumption among dwellings with identical EPR values; (2) measured consumption averaging around 30% lower than EPR predictions; (3) a growing disparity between actual and predicted performance as EPR values rise, reaching approximately 17% for dwellings with an EPR of 150 kWh/m²a to about 60% for those with an EPR of 500 kWh/m²a (Sunikka-Blank & Galvin, 2012); and (4) a reverse trend occurring for dwellings with an EPR below 100 kWh/m²a, where occupants consume more energy than initially calculated in the EPR, referred to as the rebound effect. Galvin & Sunikka-Blank (2016) identify that a combination of high prebound effect and low income is a clear indicator of fuel poverty, and suggest this metric be utilised to target retrofit policy initiatives.
Rebound Effect
The rebound effect materializes when energy-efficient buildings consume more energy than predicted. Occupants perceive less guilt associated with their energy consumption and use electrical equipment and heating systems more liberally post-retrofit, thereby diminishing the anticipated energy savings (Zoonnekindt, 2019). Santangelo & Tondelli (2017) affirm that the rebound effect arises from occupants’ reduced vigilance towards energy-related behaviours, under the presumption that enhanced energy efficiency in buildings automatically decreases consumption, regardless of usage levels and individual behaviours. Galvin (2014) further speculates several factors contributing to the rebound effect, including post-retrofit shifts in user behaviour, difficulties in operating heating controls, inadequacies in retrofit technology, or flawed mathematical models for estimating pre- and post-retrofit theoretical consumption demand. The DREEAM project, funded by the European Union, discovered instances of electrical system misuse in retrofitted homes upon evaluation (Zoonnekindt, 2019). A comprehensive comprehension of the underlying causes of the rebound effect is imperative for effective communication with all retrofit stakeholders and for addressing these issues during the early design stages.
Engaging residents in the retrofit process from the outset can serve as a powerful strategy to mitigate performance gaps. Design-thinking (Boess, 2022), design-driven approaches (Lucchi & Delera, 2020), and user-centred design (Awwal et al., 2022; van Hoof & Boerenfijn, 2018) foster socially inclusive retrofit that considers Equality, Diversity, and Inclusion (EDI). These inclusive approaches can increase usability of technical systems, empower residents to engage with retrofit and interact with energy-saving technology, and enhance residents’ energy use, cultivating sustainable energy practices as habitual behaviours. Consequently, this concerted effort not only narrows the performance gap but simultaneously enhances overall wellbeing and fortifies social sustainability within forging communities.
References
Awwal, S., Soliman-Junior, J., Ayo-Adejuyigbe, M., Tzortzopoulos, P., & Kagioglou, M. (2022). Social Housing Retrofit Living Lab: Methodological Approach. IOP Conference Series: Earth and Environmental Science, 1101(5), 052020. https://doi.org/10.1088/1755-1315/1101/5/052020
Beagon, P., Boland, F., & O’Donnell, J. (2018). Quantitative evaluation of deep retrofitted social housing using metered gas data. Energy and Buildings, 170, 242–256. https://doi.org/10.1016/j.enbuild.2018.04.022
Boess, S. (2022). Let’s Get Sociotechnical: A Design Perspective on Zero Energy Renovations. Urban Planning, 7(2), 97–107. https://doi.org/10.17645/up.v7i2.5107
Galvin, R. (2014). Making the “rebound effect” more useful for performance evaluation of thermal retrofits of existing homes: Defining the “energy savings deficit” and the “energy performance gap.” Energy and Buildings, 69, 515–524. https://doi.org/10.1016/j.enbuild.2013.11.004
Galvin, R., & Sunikka-Blank, M. (2016). Quantification of (p)rebound effects in retrofit policies - Why does it matter? Energy, 95, 415–424. https://doi.org/10.1016/j.energy.2015.12.034
Gupta, R., & Gregg, M. (2015). Do deep low carbon retrofits actually work? Energy Procedia, 78, 919–924. https://doi.org/10.1016/j.egypro.2015.11.019
Lucchi, E., & Delera, A. C. (2020). Enhancing the historic public social housing through a user-centered design-driven approach. Buildings, 10(9). https://doi.org/10.3390/BUILDINGS10090159
Santangelo, A., & Tondelli, S. (2017). Occupant behaviour and building renovation of the social housing stock: Current and future challenges. Energy and Buildings, 145, 276–283. https://doi.org/10.1016/j.enbuild.2017.04.019
Sunikka-Blank, M., & Galvin, R. (2012). Introducing the prebound effect: The gap between performance and actual energy consumption. Building Research and Information, 40(3), 260–273. https://doi.org/10.1080/09613218.2012.690952
van Hoof, J., & Boerenfijn, P. (2018). Re-inventing existing real estate of social housing for older people: Building a new De Benring in Voorst, The Netherlands. Buildings, 8(7). https://doi.org/10.3390/buildings8070089
Zoonnekindt, K. (2019). DREEAM scaling energy renovation: Final analysis on the tenants engagement and communication strategies 4.8. https://dreeam.eu/wp-content/uploads/2020/09/D.4.8_Final-analysis-on-tenants-engagement-and-communication-strategies-compressed.pdf
Created on 08-09-2023 | Update on 23-10-2024
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