What is the optimal robust environmental and cost-effective solution for building renovation? Not the usual one

Abstract Buildings are responsible for a large share of CO2 emissions in the world. Building renovation is crucial to decrease the environmental impact and meet the United Nations climate action goals. However, due to buildings’ long service lives, there are many uncertainties that might cause a deviation in the results of a predicted retrofit outcome. In this paper, we determine climate-friendly and cost-effective renovation scenarios for two typical buildings with low and high energy performance in Switzerland using a methodology of robust optmization. First, we create an integrated model for life cycle assessment (LCA) and life cycle cost analysis (LCCA). Second, we define possible renovation measures and possible levels of renovation. Third, we identify and describe the uncertain parameters related to the production, replacement and dismantling of building elements as well as the operational energy use in LCCA and LCA. Afterwards, we carry out a robust multi-objective optimization to identify optimal renovation solutions. The results show that the replacement of the heating system in the building retrofit process is crucial to decrease the environmental impact. They also show that for a building with already good energy performance, the investments are not paid off by the operational savings. The optimal solution for the building with low energy performance includes the building envelope renovation in combination with the heating system replacement. For both buildings, the optimal robust cost-effective and climate-friendly solution is different from the deep renovation practice promoted to decrease the energy consumption of a building.

[1]  Sébastien Lasvaux,et al.  Dataset of service life data for 100 building elements and technical systems including their descriptive statistics and fitting to lognormal distribution , 2021, Data in brief.

[2]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models , 2004 .

[3]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[4]  Marijke Steeman,et al.  Environmental evaluation of pareto optimal renovation strategies: a multidimensional life-cycle analysis , 2020 .

[5]  Gregor P. Henze,et al.  Uncertainty quantification for combined building performance and cost-benefit analyses , 2013 .

[6]  Pierre Hollmuller,et al.  COMPARE RENOVE : du catalogue de solutions à la performance réelle des rénovations énergétiques (écarts de performance, bonnes pratiques et enseignements tirés) , 2018 .

[7]  B. Sudret,et al.  Robust and resilient renovation solutions in different climate change scenarios , 2020, IOP Conference Series: Earth and Environmental Science.

[8]  Sébastien Lasvaux,et al.  Influence of construction material uncertainties on residential building LCA reliability , 2017 .

[9]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

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

[11]  Thomas J. Santner,et al.  Design and analysis of computer experiments , 1998 .

[12]  Sébastien Lasvaux,et al.  Statistical method to identify robust building renovation choices for environmental and economic performance , 2020, Building and Environment.

[13]  B. Sudret,et al.  Quantile-based optimization under uncertainties using adaptive Kriging surrogate models , 2016, Structural and Multidisciplinary Optimization.

[14]  R. Sonderegger MOVERS AND STAYERS: THE RESIDENT'S CONTRIBUTION TO VARIATION ACROSS HOUSES IN ENERGY CONSUMPTION FOR SPACE HEATING , 1978 .

[15]  Henk Visscher,et al.  The effect of occupancy and building characteristics on energy use for space and water heating in Dutch residential stock , 2009 .

[16]  Michele Germani,et al.  Building Retrofit Measures and Design: A Probabilistic Approach for LCA , 2018, Sustainability.

[17]  Ehsan Noroozinejad Farsangi,et al.  Supervised damage and deterioration detection in building structures using an enhanced autoregressive time-series approach , 2020 .

[18]  Stefanie Hellweg,et al.  A new method for analyzing sustainability performance of global supply chains and its application to material resources. , 2019, The Science of the total environment.

[19]  Dejan Mumovic,et al.  Implementing multi objective genetic algorithm for life cycle carbon footprint and life cycle cost minimisation: A building refurbishment case study , 2016 .

[20]  Bernhard Sendhoff,et al.  Robust Optimization - A Comprehensive Survey , 2007 .

[21]  C. Chen,et al.  Environmental impact of cement production: detail of the different processes and cement plant variability evaluation , 2010 .

[22]  M. Steeman,et al.  Environmental and financial assessment of façade renovations designed for change: developing optimal scenarios for apartment buildings in Flanders , 2020 .

[23]  A. Horvath,et al.  Can life-cycle assessment produce reliable policy guidelines in the building sector? , 2017 .

[24]  Elena Collina,et al.  Heating systems LCA: comparison of biomass-based appliances , 2013, The International Journal of Life Cycle Assessment.

[25]  John E. Mottershead,et al.  A review of robust optimal design and its application in dynamics , 2005 .

[26]  Michele Germani,et al.  Towards a probabilistic approach in LCA of building retrofit measures , 2017 .

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

[28]  Francesco Causone,et al.  Multi-objective optimization of a nearly zero-energy building based on thermal and visual discomfort minimization using a non-dominated sorting genetic algorithm (NSGA-II) , 2015 .

[29]  Robert Ries,et al.  Characterizing, Propagating, and Analyzing Uncertainty in Life‐Cycle Assessment: A Survey of Quantitative Approaches , 2007 .

[30]  Guillaume Habert,et al.  Probabilistic LCA and LCC to identify robust and reliable renovation strategies , 2019, IOP Conference Series: Earth and Environmental Science.

[31]  Gonzalo Guillén-Gosálbez,et al.  Multi-objective optimization of thermal modelled cubicles considering the total cost and life cycle environmental impact , 2015 .

[32]  Kristina Mjörnell,et al.  Approach to manage parameter and choice uncertainty in life cycle optimisation of building design: Case study of optimal insulation thickness , 2021 .

[33]  Amin Hammad,et al.  Generation of whole building renovation scenarios using variational autoencoders , 2021 .

[34]  Sébastien Lasvaux,et al.  Economic and environmental assessment of building renovation , 2016 .

[35]  Laura Gabrielli,et al.  Developing a model for energy retrofit in large building portfolios: Energy assessment, optimization and uncertainty , 2019, Energy and Buildings.

[36]  Gregor Wernet,et al.  The ecoinvent database version 3 (part I): overview and methodology , 2016, The International Journal of Life Cycle Assessment.

[37]  Tove Malmqvist,et al.  Investigation of maintenance and replacement of materials in building LCA , 2020, IOP Conference Series: Earth and Environmental Science.

[38]  C. Sánchez-Guevara,et al.  Income, energy expenditure and housing in Madrid: retrofitting policy implications , 2015 .

[39]  Gerhart I. Schuëller,et al.  Computational methods in optimization considering uncertainties – An overview , 2008 .

[40]  Amin Hammad,et al.  Developing surrogate ANN for selecting near-optimal building energy renovation methods considering energy consumption, LCC and LCA , 2019, Journal of Building Engineering.

[41]  Martin Kumar Patel,et al.  Assessment of the current thermal performance level of the Swiss residential building stock: Statistical analysis of energy performance certificates , 2018, Energy and Buildings.

[42]  G. Chiandussi,et al.  Comparison of multi-objective optimization methodologies for engineering applications , 2012, Comput. Math. Appl..

[43]  P. Bertoldi,et al.  Accelerating energy renovation investments in buildings , 2019 .

[44]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[45]  Sébastien Lasvaux,et al.  Uncertainty of building elements’ service lives in building LCA & LCC: What matters? , 2020 .

[46]  Guillaume Habert,et al.  Retrofit as a carbon sink: The carbon storage potentials of the EU housing stock , 2019, Journal of Cleaner Production.

[47]  Zsuzsa Szalay,et al.  Modular approach to multi-objective environmental optimization of buildings , 2020 .

[48]  David Parra,et al.  Analysis of space heating demand in the Swiss residential building stock: Element-based bottom-up model of archetype buildings , 2019, Energy and Buildings.

[49]  R. Vautard,et al.  EURO-CORDEX: new high-resolution climate change projections for European impact research , 2014, Regional Environmental Change.

[50]  Genichi Taguchi,et al.  Quality Engineering through Design Optimization , 1989 .

[51]  Fausto Freire,et al.  Comparative life-cycle energy analysis of a new and an existing house: The significance of occupant’s habits, building systems and embodied energy , 2016 .