Temporal optimization for affordable and resilient Passivhaus dwellings in the social housing sector
Joe Forde
Christina Hopfe
Rob McLeod
Ralph Evins
2134/11418471.v1
https://repository.lboro.ac.uk/articles/journal_contribution/Temporal_optimization_for_affordable_and_resilient_Passivhaus_dwellings_in_the_social_housing_sector/11418471
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 multiobjective 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.
2020-01-06 11:12:12
Energy
Engineering
multi-criteria optimization
decision support
social housing
affordable housing
genetic algorithm
overheating
Economics