Decision Support for existing buildings: an LCC-based proposal for facade retrofitting technological choices

The goal of this paper is to present a usable and effective tool to evaluate residential facade retrofitting solutions in early stages of design, keeping into account envelope features and installation issues. Decarbonisation goals set for 2050 impose existing building stock renovation and energy retrofit. Several drivers are available in EU Countries to trigger these operations. Nonetheless, the renovation rate in EU Member States remains low: barriers to building retrofit are identified, and a main issue in this sense is the lack of use of Decision Support Systems. DSS exist but are often neglected by building designers or owners, due to different reasons. Existing methodologies do not take into account the quantity and quality of information available at the various stages of building life cycle; furthermore, they mainly focus on energy related aspects, neglecting technological and installation related factors. This paper aims at providing an LCC-based decision framework to help decision makers in early stages of design to choose the most suitable technology for building facade retrofitting. A Utility Function expressing LCC for residential building renovation is provided, focusing on facades renovation and on installation and morphology related aspects. Information and data flow through the phases is presented and discussed, showing how the proposed method can be adapted to different stages, and testing its robustness through sensitivity and uncertainty analyses. Three main categories of renovation technologies are analysed (ventilated facade, ETICS, and prefabricated solutions). The proposed method is applied to a residential case study building. The adaptability of the tool to different stages of design is discussed, and further potential applications are presented.

[1]  Elisa Di Giuseppe,et al.  Probabilistic Life Cycle Cost Analysis of Building Energy Efficiency Measures: Selection and Characterization of the Stochastic Inputs through a Case Study☆ , 2017 .

[2]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice , 2002 .

[3]  Daniel Castro-Lacouture,et al.  GA-based decision support system for housing condition assessment and refurbishment strategies , 2009 .

[4]  A. Omer.  Energy, environment and sustainable development , 2008 .

[5]  Edmundas Kazimieras Zavadskas,et al.  Panel building refurbishment elements effective selection by applying multiple-criteria methods , 2013 .

[6]  Gonzalo Guillén-Gosálbez,et al.  Multi-objective optimization coupled with life cycle assessment for retrofitting buildings , 2014 .

[7]  Gerardo Maria Mauro,et al.  Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: A new approach to assess cost-optimality , 2016 .

[8]  Paul Cooper,et al.  Existing building retrofits: Methodology and state-of-the-art , 2012 .

[9]  Edmundas Kazimieras Zavadskas,et al.  Multivariant design and multiple criteria analysis of building refurbishments , 2005 .

[10]  João A. Labrincha,et al.  Eco-efficient Construction and Building Materials , 2011 .

[11]  Paola Sanguinetti,et al.  Integrated performance framework to guide facade retrofit , 2012 .

[12]  J. D. Balcomb,et al.  Multi-criteria decision-making process for buildings , 2000, Collection of Technical Papers. 35th Intersociety Energy Conversion Engineering Conference and Exhibit (IECEC) (Cat. No.00CH37022).

[13]  T. Konstantinou,et al.  Facade Refurbishment Toolbox. Supporting the Design of Residential Energy Upgrades , 2014, Architecture and the Built Environment.

[14]  Nuno M.M. Ramos,et al.  Towards a methodology to include building energy simulation uncertainty in the Life Cycle Cost analysis of rehabilitation alternatives , 2015 .

[15]  L. D. Danny Harvey,et al.  Reducing energy use in the buildings sector: measures, costs, and examples , 2009 .

[16]  Kristina Mjörnell,et al.  A Tool to Evaluate Different Renovation Alternatives with Regard to Sustainability , 2014 .

[17]  Y. K. Wen,et al.  Minimum Building Life-Cycle Cost Design Criteria. I: Methodology , 2001 .

[18]  J. de Brito,et al.  Refurbishment decision support tools: A review from a Portuguese user’s perspective , 2013 .

[19]  M. Santamouris,et al.  On the impact of urban climate on the energy consumption of buildings , 2001 .

[20]  M. Frey,et al.  Towards nearly zero-energy buildings: The state-of-art of national regulations in Europe , 2013 .

[21]  Gabriele Masera,et al.  Identification of Technological and Installation-related Parameters for a Multi-criteria Approach to Building Retrofit☆ , 2017 .

[22]  Stefano Paolo Corgnati,et al.  Methodology to define cost-optimal level of architectural measures for energy efficient retrofits of existing detached residential buildings in Turkey , 2016 .

[23]  Elisa Di Giuseppe,et al.  Impacts of Uncertainties in Life Cycle Cost Analysis of Buildings Energy Efficiency Measures: Application to a Case Study , 2017 .

[24]  Meral Ozel,et al.  Thermal performance and optimum insulation thickness of building walls with different structure materials , 2011 .

[25]  Ambrose Dodoo,et al.  Cost-optimum analysis of building fabric renovation in a Swedish multi-story residential building , 2014 .

[26]  B. Dhillon.  Life Cycle Costing , 1999 .

[27]  Carlos Henggeler Antunes,et al.  A comparison between cost optimality and return on investment for energy retrofit in buildings-A real options perspective , 2016 .

[28]  Umberto Berardi,et al.  Stakeholders’ influence on the adoption of energy-saving technologies in Italian homes , 2013 .

[29]  Laura Gabrielli,et al.  Evaluation of energy retrofit in buildings under conditions of uncertainty: The prominence of the discount rate , 2017 .

[30]  T. Konstantinou,et al.  Facade Refurbishment Toolbox , 2014 .

[31]  Rasmus Lund Jensen,et al.  Early stage decision support for sustainable building renovation – A review , 2016 .

[32]  Timothy L. Hemsath,et al.  Sensitivity analysis evaluating basic building geometry's effect on energy use , 2015 .

[33]  Enedir Ghisi,et al.  Uncertainty analysis of user behaviour and physical parameters in residential building performance simulation , 2014 .

[34]  I. G. Capeluto,et al.  Strategic decision-making for intelligent buildings: Comparative impact of passive design strategies and active features in a hot climate , 2008 .

[35]  George Baird,et al.  Users' perceptions of sustainable buildings – Key findings of recent studies , 2015 .

[36]  I. G. Capeluto,et al.  Advice tool for early design stages of intelligent facades based on energy and visual comfort approach , 2009 .

[37]  Dejan Mumovic,et al.  Towards measurement and verification of energy performance under the framework of the European directive for energy performance of buildings , 2014 .

[38]  Sevastianos Mirasgedis,et al.  European residential buildings and empirical assessment of the Hellenic building stock, energy consumption, emissions and potential energy savings , 2007 .