Temporal optimization for affordable and resilient Passivhaus dwellings in the social housing sector

Joe Forde, Christina J. Hopfe, Robert S. McLeod and Ralph Evins

Applied Energy, 2020, vol. 261, issue C, No S0306261919320707

Abstract: Scarcity of affordable energy efficient dwellings is a defining characteristic of the global housing crisis. In many countries this problem has been exacerbated by single objective cost-models which favour the homogeneous development of market tenures at the expense of delivering high-quality affordable homes. Despite the obvious environmental and fuel-poverty alleviation benefits of advanced energy performance standards, such as Passivhaus, they are often dismissed as an affordable housing solution due to elevated build-cost premiums. The present work attempts to reconcile this housing affordability – energy performance nexus by establishing a novel decision support framework for Passivhaus design using genetic multi-objective optimization. The use of constrained genetic algorithms coupled to the Passive House Planning Package software is shown to produce cost optimal designs which are fully compliant with the Passivhaus standard. The findings also reveal that the precise choice of Passivhaus certification criteria has significant impacts on overheating risks using future probabilistic climate data. This means that the design implications of using either the peak heating load or annual heating demand certification criteria must be temporally evaluated to ensure resilient whole-life design outcomes. In a typical UK context, the findings show that affordable Passivhaus dwelling construction costs can be reduced by up to £366/m2 (or 22% of build cost). Use of this evidence-based decision support tool could thereby enable local authorities and developers to make better-informed decisions in relation to cost optimal trade-offs between achieving advanced energy performance standards and the viability of large affordable housing developments.

Keywords: Multi-criteria optimization; Decision support; Social housing; Affordable housing; Genetic algorithm; Overheating (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (6)

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DOI: 10.1016/j.apenergy.2019.114383

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