Multi-Objective Optimization of Building Life Cycle Performance. A Housing Renovation Case Study in Northern Europe

Abstract

While the operational energy use of buildings is often regulated in current energy saving policies, their embodied greenhouse gas emissions still have a considerable mitigation potential. The study aims at developing a multi-objective optimization method for design and renovation of buildings incorporating the operational and embodied energy demands, global warming potential, and costs as objective functions. The optimization method was tested on the renovation of an apartment building in Denmark, mainly focusing envelope improvements as roof and exterior wall insulation and windows. Cellulose insulation has been the predominant result, together with fiber cement or aluminum-based cladding and 2-layered glazing. The annual energy demand has been reduced from 166.4 to a range between 76.5 and 83.7 kWh/(m2 y) in the optimal solutions. The fact that the legal requirements of 70 kWh/(m2 y) are nearly met without building service improvements indicates that energy requirements can be fulfilled without compromising greenhouse gas emissions and cost. Since the method relies on standard national performance reporting tools, the authors believe that this study is a preliminary step towards more cost-efficient and low-carbon building renovations by utilizing multi-optimization techniques.

Multicriteria-Oriented Optimization of Building Energy Performances: The Annex 72 IEA-EBC Experience

“…The studies reviewed in this work can be categorized according to different aspects. Regarding the aim of the study, most of these thirteen works analysed the refurbishment of existing buildings [9][10][11][12][13][14][15], while three of them focussed on the early design stage of residential buildings [16,17], and one just examined the optimal concrete quantity and quality, disregarding the operating phase and the energy demand for air conditioning [18]. Only one of the examined buildings was optimized to reach the plus-energy level [16], while in most of the other studies the installation of renewables and the annual energy balance were not taken into account.…”

“…Only one of the examined buildings was optimized to reach the plus-energy level [16], while in most of the other studies the installation of renewables and the annual energy balance were not taken into account. Considering the developers of the studies, six of them were performed by the research group of the Bauhaus-University Weimar [9][10][11][12]18], four studies by the group from the University of Palermo [13][14][15], two studies came from the Budapest University of Technology and Economics [17] and the last work was developed by the MINES ParisTech researchers [16]. Most of the studies [9-12, 17, 18] were developed in Rhinoceros CAD environment [19], since this tool has a user-friendly framework and allows for integrating many aspects of the building design in a unique tool through many plug-ins available on its library.…”

“…Most of the studies [9-12, 17, 18] were developed in Rhinoceros CAD environment [19], since this tool has a user-friendly framework and allows for integrating many aspects of the building design in a unique tool through many plug-ins available on its library. Two studies [13,14] were modelled on SketchUp 3D CAD [20], using other tools for the optimization; one did not require a CAD modelling phase [15] and one was entirely developed in Pleiades [21], a French tool able to manage the 3D modelling, the energy simulation, the optimization process and the life cycle assessment [16]. In the reviewed studies, the use phase energy demand of the building was assessed mainly through a dynamic building performance simulation (BPS) software, with EnergyPlus [22] being themost popular [9-11, 13, 14], since it allows for the connection with both Rhinoceros and SketchUp, while four studies [12,15,17] adopted energy calculations based on the quasi-stationary seasonal method described on the European standard EN ISO 13790 [23] or on the German standard DIN V 18599 [24].…”

“…Two studies [13,14] were modelled on SketchUp 3D CAD [20], using other tools for the optimization; one did not require a CAD modelling phase [15] and one was entirely developed in Pleiades [21], a French tool able to manage the 3D modelling, the energy simulation, the optimization process and the life cycle assessment [16]. In the reviewed studies, the use phase energy demand of the building was assessed mainly through a dynamic building performance simulation (BPS) software, with EnergyPlus [22] being themost popular [9-11, 13, 14], since it allows for the connection with both Rhinoceros and SketchUp, while four studies [12,15,17] adopted energy calculations based on the quasi-stationary seasonal method described on the European standard EN ISO 13790 [23] or on the German standard DIN V 18599 [24]. Many building performance optimization (BPO) tools and algorithms were employed in these studies: MOBO Multi-Objective Building Optimization tool [25,26] was chosen in three studies [13][14][15], three Rhinoceros plug-ins for the optimization, namely Galapagos [9,10,18], Octopus [11,17] and GOAT [11,12], and MATLAB programming environment [14] were used in the other studies.…”

“…In the reviewed studies, the use phase energy demand of the building was assessed mainly through a dynamic building performance simulation (BPS) software, with EnergyPlus [22] being themost popular [9-11, 13, 14], since it allows for the connection with both Rhinoceros and SketchUp, while four studies [12,15,17] adopted energy calculations based on the quasi-stationary seasonal method described on the European standard EN ISO 13790 [23] or on the German standard DIN V 18599 [24]. Many building performance optimization (BPO) tools and algorithms were employed in these studies: MOBO Multi-Objective Building Optimization tool [25,26] was chosen in three studies [13][14][15], three Rhinoceros plug-ins for the optimization, namely Galapagos [9,10,18], Octopus [11,17] and GOAT [11,12], and MATLAB programming environment [14] were used in the other studies. The first outcome of this review is the great variability in the adoption of software tools, since the researchers were often forced to combine many software and plug-ins Analysing the optimization approach, six studies adopted a single-objective optimization approach [9-12, 17, 18], while the remaining seven employed multiobjective algorithms [11,[13][14][15][16][17].…”