Publication | Open Access
Multi-Criteria Optimisation of an Experimental Complex of Single-Family Nearly Zero-Energy Buildings
38
Citations
45
References
2020
Year
Multi-criteria OptimisationEngineeringUrban Energy ModelingEnergy EfficiencyEnergy-efficient DesignZero-energy BuildingGreen BuildingBuilding Energy ConservationStructural OptimizationSocial SciencesSustainable DesignStructural EngineeringBuilt EnvironmentLow Energy ArchitectureSustainable ArchitectureEnergy ConsumptionOverall Energy ConsumptionBuilding EnvelopesDesignSustainable BuildingBuilding EnergyLow-energy HouseEnergy ManagementUrban LayoutConstruction ManagementExperimental ComplexConstruction Engineering
The Directive 2010/31/EU on the energy performance of buildings has introduced the standard of “nearly zero-energy buildings” (NZEBs). European requirements place the obligation to reduce energy consumption on all European Union Member States, particularly in sectors with significant energy consumption indicators. Construction is one such sector, as it is responsible for around 40% of overall energy consumption. Apart from a building’s mass and its material and installation solutions, its energy consumption is also affected by its placement relative to other buildings. A proper urban layout can also lead to a reduction in project development and occupancy costs. The goal of this article is to present a method of optimising single-family house complexes that takes elements such as direct construction costs, construction site organisation, urban layout and occupancy costs into consideration in the context of sustainability. Its authors have analysed different proposals of the placement of 40 NZEBs relative to each other and have carried out a multi-criteria analysis of the complex, determining optimal solutions that are compliant with the precepts of sustainability. The results indicated that the layout composed of semi-detached houses scored the highest among the proposed layouts under the parameter weights set by the developer. This layout also scored the highest when parameter weights were uniformly distributed during a test simulation.
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