Sustainable framework for buildings in cold regions of China considering life cycle cost and environmental impact as well as thermal comfort

Abstract In recent decades, environmental problems have enforced designers to estimate the level of environmental emission of building design and reduce their environmental impact. On the premise of ensuring indoor comfort, the cost-effectiveness of solutions for reducing the building’s greenhouse gas has become a critical issue. Based on the Life Cycle Assessment (LCA), this paper establishes a building performance trade-off framework for indoor thermal comfort, economics, and environmental implication. This framework consists of four parts: the establishment of the optimization model; sensitivity analysis; obtain of Pareto frontier solutions, and decision-making analysis. Optimization variables involve envelope type and some envelope physical parameters. The “design variables-building performances” database is obtained by using building simulation software combined with the Latin hypercube sampling algorithm. Sensitivity analysis is used to extract the key factors affecting building performance. The designer can prioritize these key factors and it can reduce the uncertainty of building performance. A multi-objective optimization method coupling Gradient Boosted Decision Tree (GBDT) and non-dominated sorting genetic (NSGA-II) algorithm is proposed to seek the trade-off between three performances (obtain Pareto frontier solutions). The Pareto solution provides a more comprehensive reference for the preferences of different stakeholders, and the set of alternative solutions is further shrunk. Finally, take a specific residential building in China’s cold climate zone as a showcase of the trade-off framework. According to the obtained Pareto frontier solution, the solution set is shrunk to a certain range, and the distribution ranges of Life Cycle Costs, the greenhouse gas emissions, and the annual thermal discomfort hour ratio are 122.3–137.1 USD/m2, 15.6–44.8 kg CO2/m2, and 19.1–25.2%, respectively. The trade-off framework adopts the order of objective Pareto optimal and then subjective preference selection, narrowing the scope of alternatives for designers and saving time-cost of decision-making.

[1]  Zheng O'Neill,et al.  The role of sensitivity analysis in the building performance analysis: A critical review , 2020 .

[2]  Farshad Kheiri,et al.  A review on optimization methods applied in energy-efficient building geometry and envelope design , 2018, Renewable and Sustainable Energy Reviews.

[3]  Bernard Yannou,et al.  Ecodesign tools in the construction sector: analyzing usage inadequacies with designers' needs , 2017 .

[4]  Nick Baker,et al.  Natural ventilation in practice: linking facade design, thermal performance, occupant perception and control , 2008 .

[5]  Philippe Rigo,et al.  A review on simulation-based optimization methods applied to building performance analysis , 2014 .

[6]  Krushna Mahapatra.  Energy use and CO2 emission of new residential buildings built under specific requirements – The case of Växjö municipality, Sweden , 2015 .

[7]  Standard Ashrae.  Thermal Environmental Conditions for Human Occupancy , 1992 .

[8]  David Moreno,et al.  Correlating daylight availability metric with lighting, heating and cooling energy consumptions , 2018 .

[9]  Louis Gosselin,et al.  Sensitivity analysis of energy performance and thermal comfort throughout building design process , 2018 .

[10]  Moncef Krarti,et al.  Optimization of envelope and HVAC systems selection for residential buildings , 2011 .

[11]  Akash Samanta,et al.  Influence of orientation and the impact of external window shading on building thermal performance in tropical climate , 2017 .

[12]  Xing Shi,et al.  A review on building energy efficient design optimization rom the perspective of architects , 2016 .

[13]  Wei Tian,et al.  A review of sensitivity analysis methods in building energy analysis , 2013 .

[14]  Somayeh Asadi,et al.  Development of a new methodology to optimize building life cycle cost, environmental impacts, and occupant satisfaction , 2017 .

[15]  Aris Tsangrassoulis,et al.  Algorithms for optimization of building design: A review , 2014 .

[16]  Holly Wasilowski Samuelson,et al.  Parametric Energy Simulation in Early Design: High-Rise Residential Buildings in Urban Contexts , 2016 .

[17]  Danny S. Parker,et al.  A framework for the cost-optimal design of nearly zero energy buildings (NZEBs) in representative climates across Europe , 2018 .

[18]  Jianlei Niu,et al.  Energy and carbon emission payback analysis for energy-efficient retrofitting in buildings—Overhang shading option , 2012 .

[19]  M. Premrov,et al.  Environmental impact assessment of building envelope components for low-rise buildings , 2018, Energy.

[20]  D. Steinberg,et al.  Computer experiments: a review , 2010 .

[21]  Hongxing Yang,et al.  A multi-stage optimization of passively designed high-rise residential buildings in multiple building operation scenarios , 2017 .

[22]  Luisa F. Cabeza,et al.  Life cycle assessment (LCA) and life cycle energy analysis (LCEA) of buildings and the building sector: A review , 2014 .

[23]  Emmanuel Bozonnet,et al.  Optimized design of low-rise commercial buildings under various climates – Energy performance and passive cooling strategies , 2018 .

[24]  Stephen Siu Yu Lau,et al.  A review of net zero energy buildings in hot and humid climates: Experience learned from 34 case study buildings , 2019, Renewable and Sustainable Energy Reviews.

[25]  Hongxing Yang,et al.  Integrated energy performance optimization of a passively designed high-rise residential building in different climatic zones of China , 2018 .

[26]  Scott Glick.  Life-cycle assessment and life-cycle costs: A framework with case study implementation focusing on residential heating systems , 2007 .

[27]  Ali F. Alajmi,et al.  Transforming a passive house into a net-zero energy house: a case study in the Pacific Northwest of the U.S. , 2018, Energy Conversion and Management.

[28]  Gerardo Maria Mauro,et al.  Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort , 2016 .

[29]  Jon C. Helton,et al.  Survey of sampling-based methods for uncertainty and sensitivity analysis , 2006, Reliab. Eng. Syst. Saf..

[30]  Víctor D. Fachinotti,et al.  An efficient metamodel-based method to carry out multi-objective building performance optimizations , 2020 .

[31]  Ondrej Krejcar,et al.  Multi-objective energy and daylight optimization of amorphous shading devices in buildings , 2019, Solar Energy.

[32]  Luis C. Dias,et al.  Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application , 2014 .

[33]  Enedir Ghisi,et al.  Uncertainty analysis of the computer model in building performance simulation , 2014 .

[34]  Rasmus Lund Jensen,et al.  A comparison of six metamodeling techniques applied to building performance simulations , 2018 .

[35]  M. Fesanghary,et al.  Design of low-emission and energy-efficient residential buildings using a multi-objective optimization algorithm , 2012 .

[36]  Robert H. Crawford,et al.  A framework for the integrated optimisation of the life cycle greenhouse gas emissions and cost of buildings , 2018, Energy and Buildings.

[37]  Wei Feng,et al.  A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost , 2020, Energy.

[38]  John S. Gero,et al.  On optimization in computer aided architectural design , 1980 .

[39]  Daniel E. Fisher,et al.  EnergyPlus: creating a new-generation building energy simulation program , 2001 .

[40]  Virgilio Ciancio,et al.  Multi-objective optimization of building retrofit in the Mediterranean climate by means of genetic algorithm application , 2020, Energy and Buildings.

[41]  Salvatore Carlucci,et al.  Review of adaptive thermal comfort models in built environmental regulatory documents , 2018, Building and Environment.

[42]  Kalyanmoy Deb,et al.  Multi-Objective Evolutionary Algorithms , 2015, Handbook of Computational Intelligence.

[43]  Shilei Lu,et al.  Passive Optimization Design Based on Particle Swarm Optimization in Rural Buildings of the Hot Summer and Warm Winter Zone of China , 2017 .

[44]  Xing Wu,et al.  Study of the environmental impacts based on the “green tax”—applied to several types of building materials , 2005 .

[45]  Tadhg S. O'Donovan,et al.  A review of the limitations of life cycle energy analysis for the design of fabric first low-energy domestic retrofits , 2019, Energy and Buildings.

[46]  Yong Sun,et al.  Multi-objective optimization for energy consumption, daylighting and thermal comfort performance of rural tourism buildings in north China , 2020, Building and Environment.

[47]  Boqiang Lin,et al.  Levelized cost of electricity (LCOE) of renewable energies and required subsidies in China , 2014 .

[48]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

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

[50]  Hongxing Yang,et al.  Approaching low-energy high-rise building by integrating passive architectural design with photovoltaic application , 2019, Journal of Cleaner Production.

[51]  Jean-Louis Scartezzini,et al.  Passive design optimization of newly-built residential buildings in Shanghai for improving indoor thermal comfort while reducing building energy demand , 2017, Energy and Buildings.

[52]  Sujeeva Setunge,et al.  Life cycle assessment and life cycle cost implications for roofing and floor designs in residential buildings , 2015 .

[53]  Alfonso Capozzoli,et al.  USE of the ANOVA approach for sensitive building energy design , 2010 .

[54]  Yi Wang,et al.  A multi-objective optimization methodology for window design considering energy consumption, thermal environment and visual performance , 2019, Renewable Energy.

[55]  Iva Kovacic,et al.  Building life cycle optimization tools for early design phases , 2015 .

[56]  Pascal Henry Biwole,et al.  Passive design optimization of low energy buildings in different climates , 2018, Energy.

[57]  Anne-Marie Tillman,et al.  Life cycle assessment of flooring materials: Case study , 1997 .