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Green Design Studio: A modular-based approach for high-performance building design

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  • Advances in Modeling and Simulation Tools
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Abstract

A modular-based Green Design Studio (GDS) platform has been developed in this study for fast and accurate performance analysis for early stage green building design. The GDS platform aims to simplify the design and analysis process by embedding performance parameters into design elements in modules and employing near-real-time model for whole building performance simulation as well as by providing an easy-to-use and intuitive user interface to assist users without extensive knowledge on building physics. The platform consists of building modules as fundamental building blocks, performance predicting models, and a user interface for visualization and interactive design. In the platform, a whole building is composed of modules organized in a hierarchical structure, including spaces, enclosures, service systems, sustainable resource systems and sites. Both physics-based and data-driven models can be used to simulate the building performance and optimize building systems. A simplified physics-based model, the Resistance–Capacitance (RC) model, has been proposed as a generic simulation model for the flows of heat, air, moisture and pollutants, which is significantly faster than conventional simulation tools such as EnergyPlus, and hence more practical for use in real-time design interaction and optimization. A pilot case study is conducted to illustrate the modular-based design approach using a section of an office building. Compared to conventional building performance analysis tools, the GDS platform can provide fast and reliable feedback on performance prediction for early design. The modular approach makes it easier to modify the building design and evaluate the potentials and contributions of various green design features and technologies.

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References

  • Amasyali K, El-Gohary NM (2018). A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews, 81: 1192–1205.

    Google Scholar 

  • ASHRAE (2013). ASHRAE Standard 169—Climatic Data for Building Design Standards. Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers.

    Google Scholar 

  • ASHRAE (2017a). Standard 189.1-2017—Standard for the Design of High-Performance Green Buildings. Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers.

    Google Scholar 

  • ASHRAE (2017b). ASHRAE Standard 55-2017—Thermal Environmental Conditions for Human Occupancy. Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers.

    Google Scholar 

  • ASHRAE (2018). ASHRAE Standard 90.2—Energy-efficient design of low-rise residential buildings. Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers.

    Google Scholar 

  • ASHRAE (2019a). ASHRAE Standard 62.2-2019—Ventilation and Acceptable Indoor Air Quality in Low-Rise Residential Buildings. Atlanta: American Society of Heating, Refrigerating and Air- Conditioning Engineers.

    Google Scholar 

  • ASHRAE (2019b). ASHRAE Standard 62.1-2019—Ventilation for Acceptable Indoor Air Quality. Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers.

    Google Scholar 

  • Autodesk (2019). Autodesk 3D Design, Engineering & Construction Software. Available at https://www.autodesk.com. Accessed 20 Feb 2020.

    Google Scholar 

  • Azhar S (2011). Building information modeling (BIM): trends, benefits, risks, and challenges for the AEC industry. Leadership and Management in Engineering, 11: 241–252.

    Google Scholar 

  • Bartosh A, Krietemeyer B (2017). Virtual environment for design and analysis (VEDA): interactive and immersive energy data visualizations for architectural design. Technology¦Architecture + Design, 1: 50–60.

    Google Scholar 

  • Bentley (2019). Building Information Modeling, BIM—OpenBuildings Designer Software. Available at https://www.bentley.com/en/products/product-line/building-design-software/openbuildingsdesigner. Accessed 13 Mar 2020.

    Google Scholar 

  • BERC (2018). China Building Energy Use 2018. Beijing: Building Energy Research Center of Tsinghua University

    Google Scholar 

  • Bianchini G, Casini M, Pepe D, Vicino A, Zanvettor GG (2019). An integrated model predictive control approach for optimal HVAC and energy storage operation in large-scale buildings. Applied Energy, 240: 327–340.

    Google Scholar 

  • Biswas MAR, Robinson MD, Fumo N (2016). Prediction of residential building energy consumption: A neural network approach. Energy, 117: 84–92.

    Google Scholar 

  • Bosseboeuf D (2015). Energy Efficiency Trends and Policies in the Household and Tertiary Sectors. An Analysis Based on the ODYSSEE and MURE Databases.

    Google Scholar 

  • Braun J, Chaturvedi N (2002). An inverse gray-box model for transient building load prediction. HVAC&R Research, 8: 73–99.

    Google Scholar 

  • BRE (2018). BREEAM New Construction 2018. Available at https://www.breeam.com/NC2018. Accessed 23 Jan 2020.

    Google Scholar 

  • BRE (2019). BREEAM Projects. Available at https://tools.breeam.com/projects/explore/map.jsp?sectionid=0&projectType=&rating=&certNo=&buildingName=&client=&developer=&certBody=&assessor=&addressPostcode=&countryId=&partid=10023&Submit=Search. Accessed 20 Feb 2020.

    Google Scholar 

  • Chen C, Zhao B (2011). Review of relationship between indoor and outdoor particles: I/O ratio, infiltration factor and penetration factor. Atmospheric Environment, 45: 275–288.

    Google Scholar 

  • Chen Y (2013). Development of an integrated process, modeling and simulation platform for performance-based design of low-energy and high IEQ buildings. PhD Thesis, Syracuse University, USA.

    Google Scholar 

  • Chen Y, Gu L, Zhang J (2015). EnergyPlus and CHAMPS-Multizone co-simulation for energy and indoor air quality analysis. Building Simulation, 8: 371–380.

    Google Scholar 

  • Davidson S (2015). Grasshopper Algorithmic Modeling for Rhino. Available at https://www.grasshopper3d.com. Accessed 20 Feb 2020.

    Google Scholar 

  • Day DB, Xiang J, Mo J, Li F, Chung M, et al. (2017). Association of ozone exposure with cardiorespiratory pathophysiologic mechanisms in healthy adults. JAMA Internal Medicine, 177: 1344.

    Google Scholar 

  • de Klerk R, Duarte AM, Medeiros DP, Duarte JP, Jorge J, et al. (2019). Usability studies on building early stage architectural models in virtual reality. Automation in Construction, 103: 104–116.

    Google Scholar 

  • Deb C, Zhang F, Yang J, Lee SE, Shah KW (2017). A review on time series forecasting techniques for building energy consumption. Renewable and Sustainable Energy Reviews, 74: 902–924.

    Google Scholar 

  • DesignBuilder (2017). DesignBuilder Software. Available at https://designbuilder.co.uk. Accessed 20 Feb 2020.

    Google Scholar 

  • Doan DT, Ghaffarianhoseini A, Naismith N, Zhang T, Ghaffarianhoseini A, et al. (2017). A critical comparison of green building rating systems. Building and Environment, 123: 243–260.

    Google Scholar 

  • DOE (2017). EnergyPlus. Available at https://energyplus.net. Accessed 20 Feb 2020.

    Google Scholar 

  • Dong B, Cao C, Lee SE (2005). Applying support vector machines to predict building energy consumption in tropical region. Energy and Buildings, 37: 545–553.

    Google Scholar 

  • EIA (2019). International Energy Outlook 2019. Energy Information Administration.

    Google Scholar 

  • Feng W, Zhang J, Kato S, Khalifa E, Zhang M, et al. (2009). Indoor and urban environmental monitoring using “Virtual Building” approach. In: Proceedings of International Conference on Healthy Building 2009, Syracuse, NY, USA.

    Google Scholar 

  • Feng W, Grunewald J, Nicolai A, Zhang C, Zhang JS (2012). CHAMPS-Multizone—A combined heat, air, moisture and pollutant simulation environment for whole-building performance analysis. HVAC & R Research, 18: 233–251.

    Google Scholar 

  • Gao H, Koch C, Wu Y (2019). Building information modelling based building energy modelling: A review. Applied Energy, 238: 320–343.

    Google Scholar 

  • Google SketchUp (2019). 3D Design Software. 3D Modeling on the Web SketchUp. Available at https://www.sketchup.com/page/homepage. Accessed 20 Feb 2020.

    Google Scholar 

  • Guo C, Gao Z, Shen J (2019). Emission rates of indoor ozone emission devices: A literature review. Building and Environment, 158: 302–318.

    Google Scholar 

  • He G, Zhang J, Hong S (2016). A new analytical model for airflow in solar chimneys based on thermal boundary layers. Solar Energy, 136: 614–621.

    Google Scholar 

  • Hirsch J (2016). DOE-2. Available at http://www.doe2.com. Accessed 24 Feb 2020.

    Google Scholar 

  • ICC (2009). International Energy Conservation Code. Available at https://codes.iccsafe.org/content/document/747?site_type=public. Accessed 24 Feb 2020.

    Google Scholar 

  • IESNA (2011). The Lighting Handbook: Reference and Application, 10th edn. New York: Illuminating Engineering Society.

    Google Scholar 

  • ILFI (2019). Living Building Challenge 4.0. Seattle, WA, USA: International Living Future Institute.

    Google Scholar 

  • IEA (2018a). World Energy Balances 2018. Paris: International Energy Agency.

    Google Scholar 

  • IEA (2018b). CO2 Emissions from Fuel Combustion 2018. Paris: International Energy Agency.

    Google Scholar 

  • IWBI (2018). WELL Building Standard. Available at https://www.wellcertified.com/certification/v2. Accessed 23 Jan 2020.

    Google Scholar 

  • IWBI (2019). WELL Building Porjects. Available at https://www.wellcertified.com/about-iwbi. Accessed 20 Feb 2020.

    Google Scholar 

  • JJH (2018). eQUEST. Available at http://www.doe2.com/equest. Accessed 29 Jun 2020.

    Google Scholar 

  • Kato S, Kono R, Zhang J, Feng K (2008). Virtual Building—An objectoriented database approach toward data-driven simulations of whole building performance. In: Proceedings of 1st International Conference on Building Energy and Environment (COBEE), Dalian, China.

    Google Scholar 

  • Klepeis NE, Nelson WC, Ott WR, Robinson JP, Tsang AM, et al. (2001). The National Human Activity Pattern Survey (NHAPS): A resource for assessing exposure to environmental pollutants. Journal of Exposure Science & Environmental Epidemiology, 11: 231–252.

    Google Scholar 

  • Ladybug Tools (2017). Ladybug Tools. Available at https://www.ladybug.tools. Accessed 8 May 2020.

    Google Scholar 

  • Larsson N (2009). The Integrated Design Process; History and Analysis The C-2000 Program.

    Google Scholar 

  • LBNL (2017). Radiance. Available at https://www.radiance-online.org. Accessed 20 Feb 2020.

    Google Scholar 

  • Li Z, Dong B (2018). Short term predictions of occupancy in commercial buildings—Performance analysis for stochastic models and machine learning approaches. Energy and Buildings, 158: 268–281.

    Google Scholar 

  • Liu D-L (2010). Particle deposition onto enclosure surfaces. In: Kohli R, Mittal KL (eds), Developments in Surface Contamination and Cleaning. Oxford, UK: William Andrew. pp. 1–56.

    Google Scholar 

  • Liu Z, Zhang R, Stenson T, Rais A, Chen W, et al. (2017). Definition of a reference residential building prototype for evaluating IAQ and energy efficiency strategies. In: Proceedings of Healthy Building 2017 Asia.

    Google Scholar 

  • Mat Daut MA, Hassan MY, Abdullah H, Rahman HA, Abdullah MP, et al. (2017). Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review. Renewable and Sustainable Energy Reviews, 70: 1108–1118.

    Google Scholar 

  • Microsoft (2020). Minecraft Official Site¦Minecraft. Available at https://www.minecraft.net/en-us. Accessed 16 Mar 2020.

    Google Scholar 

  • Newsham GR, Mancini S, Birt BJ (2009). Do LEED-certified buildings save energy? Yes, but…. Energy and Buildings, 41: 897–905.

    Google Scholar 

  • NIST (2018). Contam. Available at https://www.nist.gov/servicesresources/software/contam. Accessed 24 Feb 2020.

    Google Scholar 

  • Niu S, Pan W, Zhao Y (2016). A virtual reality integrated design approach to improving occupancy information integrity for closing the building energy performance gap. Sustainable Cities and Society, 27: 275–286.

    Google Scholar 

  • NREL (2017). OpenStudio. Available at https://www.openstudio.net. Accessed 20 Feb 2020.

    Google Scholar 

  • Pelken PM, Zhang J, Chen Y, Rice DJ, Meng Z, et al. (2013). “Virtual Design Studio”—Part 1: Interdisciplinary design processes. Building Simulation, 6: 235–251.

    Google Scholar 

  • Pérez-Lombard L, Ortiz J, Pout C (2008). A review on buildings energy consumption information. Energy and Buildings, 40: 394–398.

    Google Scholar 

  • Qin M, Zhang J (2020). Annex 68 Subtask 2 Final Report: Indoor Pollution Loads.

    Google Scholar 

  • Rahman SMM (2019). Simplified 3R2C building thermal network model: A case study. International Journal of Structural and Construction Engineering, 13(6): 288–294.

    Google Scholar 

  • Ramallo-González AP, Eames ME, Coley DA (2013). Lumped parameter models for building thermal modelling: An analytic approach to simplifying complex multi-layered constructions. Energy and Buildings, 60: 174–184.

    Google Scholar 

  • Robinson C, Dilkina B, Hubbs J, Zhang W, Guhathakurta S, et al. (2017). Machine learning approaches for estimating commercial building energy consumption. Applied Energy, 208: 889–904.

    Google Scholar 

  • Roelofsen P (2018). A new methodology for the evaluation of the perceived air quality depending on the air pollution, caused by human bioeffluents, the temperature, the humidity as well as the air velocity. Intelligent Buildings International, 10: 154–161.

    Google Scholar 

  • Schweizer C, Edwards RD, Bayer-Oglesby L, Gauderman WJ, Ilacqua V, et al. (2007). Indoor time–microenvironment–activity patterns in seven regions of Europe. Journal of Exposure Science & Environmental Epidemiology, 17: 170–181.

    Google Scholar 

  • Seem JE, Klein SA, Beckman WA, Mitchell JW (1989). Transfer functions for efficient calculation of multidimensional transient heat transfer. Journal of Heat Transfer, 111: 5–12.

    Google Scholar 

  • Shao M, Wang X, Bu Z, Chen X, Wang Y (2020). Prediction of energy consumption in hotel buildings via support vector machines. Sustainable Cities and Society, 57: 102128.

    Google Scholar 

  • Shen J, Gao Z (2018). Ozone removal on building material surface: A literature review. Building and Environment, 134: 205–217.

    Google Scholar 

  • Shen J, Zhang J (2019). An approach to develop a green building technology database for residential buildings. IOP Conference Series: Materials Science and Engineering, 609: 042063.

    Google Scholar 

  • Sun Y, Hou J, Cheng R, Sheng Y, Zhang X, et al. (2019). Indoor air quality, ventilation and their associations with sick building syndrome in Chinese homes. Energy and Buildings, 197: 112–119.

    Google Scholar 

  • Taleghani M, Tenpierik M, Kurvers S, van den Dobbelsteen A (2013). A review into thermal comfort in buildings. Renewable and Sustainable Energy Reviews, 26: 201–215.

    Google Scholar 

  • Tang R, Wang S (2019). Model predictive control for thermal energy storage and thermal comfort optimization of building demand response in smart grids. Applied Energy, 242: 873–882.

    Google Scholar 

  • TRNSYS (2019). TRNSYS: Transient System Simulation Tool. Available at http://www.trnsys.com. Accessed 24 Feb 2020.

    Google Scholar 

  • Unity (2020). Unity Real-Time Development Platform¦3D, 2D VR & AR Visualizations. Available at https://unity.com. Accessed 30 Apr 2020.

    Google Scholar 

  • USEPA (2017). National Ambient Air Quality Standards. Washington DC: United States Environmental Protection Agency.

    Google Scholar 

  • USEPA (2020). Air Data: Air Quality Data Collected at Outdoor Monitors Across the US. Washington DC: United States Environmental Protection Agency.

    Google Scholar 

  • USGBC (2013). LEED rating system. Available at https://www.usgbc.org/leed. Accessed 22 Jan 2020.

    Google Scholar 

  • USGBC (2019). Projects. U.S. Green Building Council. Available at https://www.usgbc.org/projects%0Ahttps://www.usgbc.org/projects/new-construction?keys=Nigeria. Accessed 20 Feb 20.

    Google Scholar 

  • Wang S, Xu X (2006). Simplified building model for transient thermal performance estimation using GA-based parameter identification. International Journal of Thermal Sciences, 45: 419–432.

    Google Scholar 

  • Wei Y, Zhang X, Shi Y, Xia L, Pan S, Wu J, Han M, Zhao X (2018). A review of data-driven approaches for prediction and classification of building energy consumption. Renewable and Sustainable Energy Reviews, 82: 1027–1047.

    Google Scholar 

  • WHO (2005). WHO Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide. Geneva: World Health Organization.

    Google Scholar 

  • Wilson E, Engebrecht-Metzger C, Horowitz S, Hendron R (2014) Building America House Simulation Protocols. Golden, CO, USA: National Renewable Energy Laboratory.

    Google Scholar 

  • Xu X, Wang S (2007). Optimal simplified thermal models of building envelope based on frequency domain regression using genetic algorithm. Energy and Buildings, 39: 525–536.

    Google Scholar 

  • You R, Zhao B, Chen C (2012). Developing an empirical equation for modeling particle deposition velocity onto inclined surfaces in indoor environments. Aerosol Science and Technology, 46: 1090–1099.

    Google Scholar 

  • Zhang J, Pelken PM, Chen Y, Rice DJ, Meng Z, et al. (2013). “Virtual Design Studio”—Part 2: Introduction to overall and software framework. Building Simulation, 6: 253–268.

    Google Scholar 

  • Zhao H, Magoulès F (2012). A review on the prediction of building energy consumption. Renewable and Sustainable Energy Reviews, 16: 3586–3592.

    Google Scholar 

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Acknowledgements

This research was supported by Syracuse University and Syracuse Center of Excellence in collaboration with Nanjing University. The authors would also like to thank Rongzhu Gu and Bryce Edwards for their assistance with the VR demonstration.

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Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, Syracuse University, 263 Link Hall, Syracuse, NY, 13244, USA

    Jialei Shen & Jianshun Zhang

  2. School of Architecture, Syracuse University, Slocum Hall, Syracuse, NY, 13244, USA

    Bess Krietemeyer & Amber Bartosh

  3. School of Architecture and Urban Planning, Nanjing University, 22 Hankou Road, Nanjing, Jiangsu Province, 210093, China

    Zhi Gao & Jianshun Zhang

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  1. Jialei Shen
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  2. Bess Krietemeyer
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  4. Zhi Gao
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  5. Jianshun Zhang
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Correspondence to Jianshun Zhang.

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Shen, J., Krietemeyer, B., Bartosh, A. et al. Green Design Studio: A modular-based approach for high-performance building design. Build. Simul. 14, 241–268 (2021). https://doi.org/10.1007/s12273-020-0728-9

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