Multi-objective optimization of the multi-story residential building with passive design strategy in South Korea

Abstract In this study, the multi-objective optimization of passive design strategy was conducted for multi-story residential buildings in South Korea. The performance of passive design was analyzed in terms of energy, environmental, and economic metrics, considering the life cycle performance. Various passive design factors were considered to better reflect the actual phenomenon, and a novel simulation modeling method which includes building model simplification and meta model was developed for an accurate and fast calculation of optimization. The sensitivity analysis was conducted to decide critical design factors in optimization and showed that the most influential design factors are airtightness, occupants, and window-to-wall ratio. Throughout optimization, the energy, environmental impact, and economic feasibility in multi-story residential building can be improved by 52.7%, 39.5%, and 36.9%, respectively. The uncertainty of optimal solution was analyzed though the uncertainty analysis. If the utility cost increases and energy system efficiency improves in the future, the optimal passive design solution will not change significantly in energy and economic feasibility, however it may become more critical in the environmental impact.

[1]  Tansu Alpcan,et al.  Influence of building envelopes, climates, and occupancy patterns on residential HVAC demand , 2019, Journal of Building Engineering.

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

[3]  Steffen Petersen,et al.  Choosing the appropriate sensitivity analysis method for building energy model-based investigations , 2016 .

[4]  Yang Wang,et al.  A state of art of review on interactions between energy performance and indoor environment quality in Passive House buildings , 2017 .

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

[6]  Ali Mostafaeipour,et al.  Energy saving evaluation of passive systems for residential buildings in hot and dry regions , 2017 .

[7]  Enrico Fabrizio,et al.  EDeSSOpt – Energy Demand and Supply Simultaneous Optimization for cost-optimized design: Application to a multi-family building , 2019, Applied Energy.

[8]  Iftekhar A. Karimi,et al.  Design of computer experiments: A review , 2017, Comput. Chem. Eng..

[9]  Manfred Hegger,et al.  Construction Materials Manual , 2002 .

[10]  Dominique Marchio,et al.  A comparison of methods for uncertainty and sensitivity analysis applied to the energy performance of new commercial buildings , 2018 .

[11]  Pieter de Wilde,et al.  A review of uncertainty analysis in building energy assessment , 2018, Renewable and Sustainable Energy Reviews.

[12]  Ksenia Petrichenko,et al.  Towards zero-emission efficient and resilient buildings.: Global Status Report , 2016 .

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

[14]  Harry Giles,et al.  Solar shading performance of window with constant and dynamic shading function in different climate zones , 2017 .

[15]  Arild Gustavsen,et al.  Windows in the Buildings of Tomorrow; Energy Losers or Energy Gainers? , 2013 .

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

[17]  A. Kilaire,et al.  Design of a prefabricated passive and active double skin façade system for UK offices , 2017 .

[18]  Poul Alberg Østergaard,et al.  Active and passive cooling methods for dwellings: A review , 2018 .

[19]  Rakhyun Kim,et al.  Analysis of Embodied Environmental Impacts of Korean Apartment Buildings Considering Major Building Materials , 2018 .

[20]  P. Salagnac,et al.  Indexes for passive building design in urban context – indoor and outdoor cooling potentials , 2018, Energy and Buildings.

[21]  Tae Hyoung Kim,et al.  Environmental Impact Analysis of Acidification and Eutrophication Due to Emissions from the Production of Concrete , 2016 .

[22]  Evina Giouri,et al.  Zero energy potential of a high-rise office building in a Mediterranean climate: Using multi-objective optimization to understand the impact of design decisions towards zero-energy high-rise buildings , 2020 .

[23]  Wei Xu,et al.  Application and suitability analysis of the key technologies in nearly zero energy buildings in China , 2019, Renewable and Sustainable Energy Reviews.

[24]  Taehoon Hong,et al.  A multi-objective optimization model for determining the building design and occupant behaviors based on energy, economic, and environmental performance , 2019, Energy.

[25]  Fariborz Haghighat,et al.  Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network , 2010 .

[26]  Xi Chen,et al.  Parametric study of passive design strategies for high-rise residential buildings in hot and humid climates: miscellaneous impact factors , 2017 .

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

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

[29]  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.

[30]  Jun Xu,et al.  A systematic approach for energy efficient building design factors optimization , 2015 .

[31]  Jiangjiang Wang,et al.  Review on multi-criteria decision analysis aid in sustainable energy decision-making , 2009 .

[32]  Ulrich Knaack,et al.  Passive cooling & climate responsive façade design , 2018, Energy and Buildings.

[33]  Anh Tuan Nguyen,et al.  A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems , 2016 .

[34]  José Manuel Cejudo López,et al.  Uncertainties and sensitivity analysis in building energy simulation using macroparameters , 2013 .

[35]  Jungmann Choi,et al.  Multi-stage optimization and meta-model analysis with sequential parameter range adjustment for the low-energy house in Korea , 2020 .

[36]  Thomas Olofsson,et al.  Exploring the trade-off in life cycle energy of building retrofit through optimization , 2020 .

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

[38]  Kamaruzzaman Sopian,et al.  The role of window glazing on daylighting and energy saving in buildings , 2015 .

[39]  B. Lei,et al.  Collaborative optimization between passive design measures and active heating systems for building heating in Qinghai-Tibet plateau of China , 2020 .

[40]  Omid Mohseni,et al.  Minimizing delivered energy and life cycle cost using Graphical script: An office building retrofitting case , 2020 .

[41]  Yeonsook Heo,et al.  Sensitivity analysis methods for building energy models: Comparing computational costs and extractable information , 2016 .

[42]  Moncef Krarti,et al.  Comparative evaluation of optimal energy efficiency designs for French and US office buildings , 2015 .

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

[44]  Hoseong Lee,et al.  Multi-criteria evaluation of medium-sized residential building with micro-CHP system in South Korea , 2019, Energy and Buildings.

[45]  Seungjun Roh,et al.  Evaluating the embodied environmental impacts of major building tasks and materials of apartment buildings in Korea , 2017 .

[46]  Enrico Benetto,et al.  Global sensitivity analysis as a support for the generation of simplified building stock energy models , 2017 .

[47]  Ruey Lung Hwang,et al.  Future trends of residential building cooling energy and passive adaptation measures to counteract climate change: The case of Taiwan , 2016 .

[48]  Yongjun Sun,et al.  Sensitivity analysis of macro-parameters in the system design of net zero energy building , 2015 .

[49]  Taehoon Hong,et al.  Establishing environmental benchmarks to determine the environmental performance of elementary school buildings using LCA , 2016 .

[50]  Jung-Hyun Yoo,et al.  Development of methodology for estimating electricity use in residential sectors using national statistics survey data from South Korea , 2014 .

[51]  Weimin Wang,et al.  Applying multi-objective genetic algorithms in green building design optimization , 2005 .

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

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

[54]  Kaamran Raahemifar,et al.  Application of passive wall systems for improving the energy efficiency in buildings: A comprehensive review , 2016 .

[55]  Virgilio Ciancio,et al.  Effects of local conditions on the multi-variable and multi-objective energy optimization of residential buildings using genetic algorithms , 2020 .

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

[57]  Fu Xiao,et al.  An interactive building power demand management strategy for facilitating smart grid optimization , 2014 .

[58]  Hoseong Lee,et al.  Optimization of dynamic poly-generation system and evaluation of system performance in building application , 2019 .

[59]  Zheng O'Neill,et al.  Uncertainty and sensitivity analysis of spatio-temporal occupant behaviors on residential building energy usage utilizing Karhunen-Loève expansion , 2017 .

[60]  Luis C. Dias,et al.  A multi-objective optimization model for building retrofit strategies using TRNSYS simulations, GenOpt and MATLAB , 2012 .

[61]  José Dinis Silvestre,et al.  Integration of LCA and LCC analysis within a BIM-based environment , 2019, Automation in Construction.

[62]  Young Wook Choi,et al.  Effect of control strategy on performance and emissions of natural gas engine for cogeneration system , 2015 .

[63]  Gerardo Maria Mauro,et al.  Retrofit of villas on Mediterranean coastlines: Pareto optimization with a view to energy-efficiency and cost-effectiveness , 2019, Applied Energy.

[64]  Gerardo Maria Mauro,et al.  Building envelope design: Multi-objective optimization to minimize energy consumption, global cost and thermal discomfort. Application to different Italian climatic zones , 2019, Energy.

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