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

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.

[1]  S. Payne.  U.S. Department of Energy , 2020, Strategies for Accelerating Cleanup at Toxic Waste Sites.

[2]  Gerardo Maria Mauro,et al.  A real industrial building: Modeling, calibration and Pareto optimization of energy retrofit , 2020 .

[3]  A. E. Eiben,et al.  Introduction to Evolutionary Computing 2nd Edition , 2020 .

[4]  K. Yetilmezsoy,et al.  Life cycle assessment of the building industry: An overview of two decades of research (1995–2018) , 2020 .

[5]  R. Frischknecht,et al.  Embodied GHG emissions of buildings – The hidden challenge for effective climate change mitigation , 2020 .

[6]  Harpa Birgisdottir,et al.  Early Design Stage Building LCA using The LCAbyg Tool: New Strategies For Bridging The Data Gap , 2019, IOP Conference Series: Earth and Environmental Science.

[7]  P. Biwole,et al.  Optimal design of renewable energy solution sets for net zero energy buildings , 2019, Energy.

[8]  Harpa Birgisdottir,et al.  Development of LCAbyg: A National Life Cycle Assessment Tool for Buildings in Denmark , 2019, IOP Conference Series: Earth and Environmental Science.

[9]  Eleonora Riva Sanseverino,et al.  Multi-Objective Building Envelope Optimization through a Life Cycle Assessment Approach , 2019, 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe).

[10]  M. Cellura,et al.  Multi-Objective Optimization of Urban Microgrid Energy Supply According to Economic and Environmental Criteria , 2019, 2019 IEEE Milan PowerTech.

[11]  Andrea Campioli,et al.  LCA benchmarks for residential buildings in Northern Italy and Denmark – learnings from comparing two different contexts , 2019, Building Research & Information.

[12]  Eleonora Riva Sanseverino,et al.  A review on optimization and cost-optimal methodologies in low-energy buildings design and environmental considerations , 2019, Sustainable Cities and Society.

[13]  B. Kiss,et al.  The applicability of different energy performance calculation methods for building life cycle environmental optimization , 2018, International Review of Applied Sciences and Engineering.

[14]  Per Anker Jensen,et al.  Indicators for quantifying environmental building performance: A systematic literature review , 2018, Journal of Building Engineering.

[15]  Gaetano Zizzo,et al.  Urban Energy Hubs Economic Optimization and Environmental Comparison in Italy and Vietnam , 2018, 2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI).

[16]  Pascal Henry Biwole,et al.  Multi-Objective Optimization Methodology for Net Zero Energy Buildings , 2018 .

[17]  Thomas Lützkendorf,et al.  IEA EBC Annex 57 ‘Evaluation of Embodied Energy and CO2eq for Building Construction’ , 2017 .

[18]  Miriam Sanchez Mayoral,et al.  Energirenovering af Traneparken , 2017 .

[19]  Anna Marszal-Pomianowska,et al.  Integrated Building Energy Design of a Danish Office Building Based on Monte Carlo Simulation Method , 2017 .

[20]  Kari Alanne,et al.  A life cycle approach to optimizing carbon footprint and costs of a residential building , 2017 .

[21]  Pradip P. Kalbar,et al.  Life cycle based dynamic assessment coupled with multiple criteria decision analysis: A case study of determining an optimal building insulation level , 2017 .

[22]  Bo Nørregaard Jørgensen,et al.  Deep Energy Renovation of the Mærsk Office Building in Denmark Using a Holistic Design Approach , 2017 .

[23]  Martin Kumar Patel,et al.  Techno-economic potential of large-scale energy retrofit in the Swiss residential building stock , 2017 .

[24]  Juha Jokisalo,et al.  Energy performance and environmental impact analysis of cost-optimal renovation solutions of large panel apartment buildings in Finland , 2017 .

[25]  Kari Alanne,et al.  A multi-objective life cycle approach for optimal building design: A case study in Finnish context , 2017 .

[26]  Jean Perron,et al.  A Constraint-Handling Technique for Genetic Algorithms using a Violation Factor , 2016, J. Comput. Sci..

[27]  Bernardette Soust-Verdaguer,et al.  Simplification in life cycle assessment of single-family houses: A review of recent developments , 2016 .

[28]  Moncef Krarti,et al.  Evaluation of net-zero energy residential buildings in the MENA region , 2016 .

[29]  Jesper Kragh,et al.  Potential heat savings during ongoing renovation of buildings until 2050 , 2016 .

[30]  Ove Mørck,et al.  Energy consumption in an old residential building before and after deep energy renovation , 2015 .

[31]  J. Kolarik,et al.  Indoor environment and energy consumption optimization using field measurements and building energy simulation , 2015 .

[32]  Toke Rammer Nielsen,et al.  Building energy optimization in the early design stages: A simplified method , 2015 .

[33]  Kim Bjarne Wittchen,et al.  Energy performance requirements using cost-optimal levels: Overview and outcomes , 2015 .

[34]  Erik Brandt,et al.  Levetider af bygningsdele ved vurdering af bæredygtighed og totaløkonomi , 2013 .

[35]  Mohamed Hamdy,et al.  Mobo A New Software For Multi-objective Building Performance Optimization , 2013, Building Simulation Conference Proceedings.

[36]  Helmuth Kreiner,et al.  Assessment of the environmental performance of buildings: A critical evaluation of the influence of technical building equipment on residential buildings , 2012, The International Journal of Life Cycle Assessment.

[37]  Jørgen Rose,et al.  DS 418:2011:Calculation of heat loss from buildings , 2011 .

[38]  Kalyanmoy Deb,et al.  Omni-optimizer: A Procedure for Single and Multi-objective Optimization , 2005, EMO.

[39]  Maurizio Cellura,et al.  Eco-sustainable energy and environmental strategies in design for recycling: the software “ENDLESS” , 2003 .

[40]  Bill Hunter,et al.  European Commission , 1992, The International Encyclopedia of Higher Education Systems and Institutions.

[41]  H. Auracher.  Thermal design and optimization , 1996 .

[42]  W. Howe,et al.  Consulting , 1994 .

[43]  Bo Nørregaard Jørgensen,et al.  A dynamic energy performance-driven approach for assessment of buildings energy Renovation—Danish case studies , 2018 .

[44]  Kim Haugbølle,et al.  Designing LCCbyg: A Tool for Economic Sustainability , 2016 .

[45]  Harpa Birgisdottir,et al.  Life Cycle Environmental Impacts from Refurbishment Projects - A Case Study , 2016 .

[46]  European Commission,et al.  Guidelines accompanying Commission Delegated Regulation (EU) No 244/2012 of 16 January 2012 supplementing Directive 2010/31/EU of the European Parliament and of the Council on the energy performance of buildings by establishing a comparative methodology framework for calculating cost-optimal levels , 2012 .

[47]  Michael O'Neill,et al.  Introduction to Evolutionary Computing , 2004, Evolutionary Computation.

[48]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[49]  Schloss Birlinghoven,et al.  How Genetic Algorithms Really Work I.mutation and Hillclimbing , 2022 .