Optimizing Preliminary Design of New Buildings with Integrated Onsite Renewable Energy Systems
Publication: Journal of Architectural Engineering
Volume 30, Issue 3
Abstract
Federal and state governments have recently expanded their regulatory mandates and incentives to promote renewable energy (RE) use in their planned new buildings. This requires planners to analyze and optimize their preliminary design decisions, such as building dimensions, orientation, location, and window-to-wall ratio, to maximize the use of RE in their buildings. To support designers in this critical task, this paper presents a novel model for optimizing preliminary building designs. The two optimization objectives of the optimization model focus on maximizing harvested RE and minimizing construction cost by identifying optimal building dimensions, orientation, window-to-wall ratio, and site layout. The optimization model complies with all design requirements, such as building dimensions, natural lighting, and cost-effectiveness constraints. The model performance is analyzed using two application examples to illustrate its capabilities in considering the impact of the surrounding environment on design decisions. The results of this analysis confirm the model contributions in identifying a set of nondominated optimal solutions that provide tradeoffs among the two optimization objectives of the developed model. These capabilities are expected to support building planners in identifying an optimal preliminary building design that maximizes the use of RE while minimizing all related construction costs.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
References
Abdallah, M., and K. El-Rayes. 2016. “Multiobjective optimization model for maximizing sustainability of existing buildings.” J. Manage. Eng. 32 (4): 1–13. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000425.
Acosta-Acosta, D. F., and K. El-Rayes. 2020. “Optimal design of classroom spaces in naturally-ventilated buildings to maximize occupant satisfaction with human bioeffluents/body odor levels.” Build. Environ. 169: 106543. https://doi.org/10.1016/j.buildenv.2019.106543.
Baek, S. H., and B. H. Lee. 2019. “Optimal decision-making of renewable energy systems in buildings in the early design stage.” Sustainability 11 (5): 1471. https://doi.org/10.3390/su11051471.
Caruso, G., F. Fantozzi, and F. Leccese. 2013. “Optimal theoretical building form to minimize direct solar irradiation.” Sol. Energy 97: 128–137. https://doi.org/10.1016/j.solener.2013.08.010.
Chau, K. W., S. K. Wong, Y. Yau, and A. K. C. Yeung. 2007. “Determining optimal building height.” Urban Stud. 44 (3): 591–607. https://doi.org/10.1080/00420980601131902.
Dino, I. G., and G. Üçoluk. 2017. “Multiobjective design optimization of building space layout, energy, and daylighting performance.” J. Comput. Civ. Eng. 31 (5): 04017025. https://doi.org/10.1061/(asce)cp.1943-5487.0000669.
DOE (Department of Energy). 2010. Energyplus documentation. Washington, DC: Department of Energy.
DOE (Department of Energy). 2020. “Chapter 5: Increasing efficiency of building systems and technologies.” In Quadrennial technology review: An assessment of energy technologies and research opportunities. Washington, DC: Department of Energy.
DOE (Department of Energy). 2022. EnergyPlus™ Version 22.1.0 Documentation. Washington, DC: Department of Energy.
DSIRE. 2016. “Database of state incentives for renewables & efficiency®—DSIRE.” Accessed March 29, 2017. http://www.dsireusa.org/.
EIA (Energy Information Administration). 2020. Monthly energy review—September 2020. Washington, DC: Energy Information Administration.
EnergyPus. n.d. “Weather data by location.” Accessed July 19, 2020. https://energyplus.net/weather-location/north_and_central_america_wmo_region_4/USA/IL/USA_IL_University.of.Illinois-Willard.AP.725315_TMY3.
Gil-García, I. C., M. S. García-Cascales, and A. Molina-García. 2022. “Urban wind: An alternative for sustainable cities.” Energies (Basel) 15 (13): 4759. https://doi.org/10.3390/en15134759.
Gordian. 2020. Square foot costs with RSmeans data. Rockland, MA: Gordian.
Granadeiro, V., J. P. Duarte, J. R. Correia, and V. M. S. Leal. 2013. “Building envelope shape design in early stages of the design process: Integrating architectural design systems and energy simulation.” Autom. Constr. 32: 196–209. https://doi.org/10.1016/j.autcon.2012.12.003.
Hassan, A. A., and K. El-Rayes. 2021. “Optimizing the integration of renewable energy in existing buildings.” Energy Build. 238: 110851. https://doi.org/10.1016/j.enbuild.2021.110851.
Ishugah, T. F., Y. Li, R. Z. Wang, and J. K. Kiplagat. 2014. “Advances in wind energy resource exploitation in urban environment: A review.” Renewable Sustainable Energy Rev. 37: 613–626. https://doi.org/10.1016/j.rser.2014.05.053.
Jin, J. T., and J. W. Jeong. 2014. “Optimization of a free-form building shape to minimize external thermal load using genetic algorithm.” Energy Build. 85: 473–482. https://doi.org/10.1016/j.enbuild.2014.09.080.
Kämpf, J. H., and D. Robinson. 2010. “Optimisation of building form for solar energy utilisation using constrained evolutionary algorithms.” Energy Build. 42 (6): 807–814. https://doi.org/10.1016/j.enbuild.2009.11.019.
Kc, A., J. Whale, and T. Urmee. 2018. “Urban wind conditions and small wind turbines in the built environment: A review.” Renewable Energy 131: 268–283. https://doi.org/10.1016/j.renene.2018.07.050.
Kiss, B., and Z. Szalay. 2020. “Modular approach to multi-objective environmental optimization of buildings.” Autom. Constr. 111: 103044. https://doi.org/10.1016/j.autcon.2019.103044.
Lin, Y., and W. Yang. 2018. “Application of multi-objective genetic algorithm based simulation for cost-effective building energy efficiency design and thermal comfort improvement.” Front. Energy Res. 6: 25. https://doi.org/10.3389/fenrg.2018.00025.
Liu, B., and D. Rodriguez. 2021. “Renewable energy systems optimization by a new multi-objective optimization technique: A residential building.” J. Build. Eng. 35: 102094. https://doi.org/10.1016/j.jobe.2020.102094.
Lydia, M., S. S. Kumar, A. I. Selvakumar, and G. E. Prem Kumar. 2014. “A comprehensive review on wind turbine power curve modeling techniques.” Renewable Sustainable Energy Rev. 30: 452–460. https://doi.org/10.1016/j.rser.2013.10.030.
Magrassi, F., A. Del Borghi, M. Gallo, C. Strazza, and M. Robba. 2016. “Optimal planning of sustainable buildings : Integration of life cycle assessment and optimization in a decision support system (DSS).” Energies (Basel) 9 (7): 490. https://doi.org/10.3390/en9070490.
Manwell, J. F., J. G. McGowan, and A. L. Rogers. 2009. Wind energy explained : Theory, design and application. Chichester, UK: Wiley.
Meyers, J., and C. Meneveau. 2012. “Optimal turbine spacing in fully developed wind farm boundary layers.” Wind Energy 15 (2): 305–317. https://doi.org/10.1002/we.469.
Mustafa, J., F. A. Almehmadi, S. Alqaed, and M. Sharifpur. 2022. “Building a sustainable energy community: Design and integrate variable renewable energy systems for rural communities.” Sustainability 14 (21): 13792. https://doi.org/10.3390/su142113792.
NREL (National Renewable Energy Laboratory). 2012. Guide to integrating renewable energy in federal construction. Washington, DC: National Institute of Building Sciences.
Oldfield, P., D. Trabucco, and A. Wood. 2009. “Five energy generations of tall buildings: An historical analysis of energy consumption in high-rise buildings.” J. Archit. 14 (5): 591–613. https://doi.org/10.1080/13602360903119405.
Ouarghi, R., and M. Krarti. 2006. “Building shape optimization using neural network and genetic algorithm approach.” ASHRAE Trans. 112 (PART 1): 484–491.
Pathirana, S., A. Rodrigo, and R. Halwatura. 2019. “Effect of building shape, orientation, window to wall ratios and zones on energy efficiency and thermal comfort of naturally ventilated houses in tropical climate.” Int. J. Energy Environ. Eng. 10 (1): 107–120. https://doi.org/10.1007/s40095-018-0295-3.
Rajeev, S., and C. S. Krishnamoorthy. 1992. “Discrete optimization of structures using genetic algorithms.” J. Struct. Eng. 118 (5): 1233–1250. https://doi.org/10.1061/(ASCE)0733-9445(1992)118:5(1233).
Rodrigues, E., A. R. Gaspar, and Á Gomes. 2014. “Improving thermal performance of automatically generated floor plans using a geometric variable sequential optimization procedure.” Appl. Energy 132: 200–215. https://doi.org/10.1016/j.apenergy.2014.06.068.
RSmeans. 2020. Square foot cost RSMeans DATA 2020. Greenville, SC: Gordian.
Schwartz, Y., R. Raslan, I. Korolija, and D. Mumovic. 2021. “A decision support tool for building design: An integrated generative design, optimisation and life cycle performance approach.” Int. J. Archit. Comput. 19 (3): 401–430. https://doi.org/10.1177/1478077121999802/FORMAT/EPUB.
Tasneem, Z., A. Al Noman, S. K. Das, D. K. Saha, M. Robiul Islam, M. Firoj Ali, M. R. Faisal Badal, M. Hafiz Ahamed, S. I. Moyeen, and F. Alam. 2020. “An analytical review on the evaluation of wind resource and wind turbine for urban application: Prospect and challenges.” Dev. Built Environ. 4: 100033. https://doi.org/10.1016/j.dibe.2020.100033.
Tuhus-Dubrow, D., and M. Krarti. 2010. “Genetic-algorithm based approach to optimize building envelope design for residential buildings.” Build Environ. 45 (7): 1574–1581. https://doi.org/10.1016/j.buildenv.2010.01.005.
U.S GSA (U.S. General Service administration). n.d. “Interior illumination levels.” U.S. General Service Administration. Accessed July 21, 2022. https://www.gsa.gov/node/82715.
van Rossum, G. 1995. “Python reference manual (1995)” Accessed March 1, 2020. https://www.narcis.nl/publication/RecordID/oai:cwi.nl:5008.
Wang, W., H. Rivard, and R. Zmeureanu. 2006. “Floor shape optimization for green building design.” Adv. Eng. Inf. 20 (4): 363–378. https://doi.org/10.1016/j.aei.2006.07.001.
Wang, Y., and C. Wei. 2021. “Design optimization of office building envelope based on quantum genetic algorithm for energy conservation.” J. Build. Eng. 35: 102048. https://doi.org/10.1016/J.JOBE.2020.102048.
WBDG (Whole Building Design Guide). n.d. “Optimize energy use.” Accessed November 19, 2021. https://www.wbdg.org/design-objectives/sustainable/optimize-energy-use.
WRCC (Western Regional Climate Center). n.d. “WRCC wind rose summary form.” Accessed April 6, 2021. https://wrcc.dri.edu/cgi-bin/wea_windrose.pl?laKCMI.
Yang, M.-D., M.-D. Lin, Y.-H. Lin, and K.-T. Tsai. 2016. “Multiobjective optimization design of green building envelope material using a non-dominated sorting genetic algorithm.” Appl. Therm. Eng. 111: 1255–1264. https://doi.org/10.1016/j.applthermaleng.2016.01.015.
Yeretzian, A., H. Partamian, M. Dabaghi, and R. Jabr. 2020. “Integrating building shape optimization into the architectural design process.” Archit. Sci. Rev. 63 (1): 63–73. https://doi.org/10.1080/00038628.2019.1689912.
Youssef, A. M. A., Z. J. Zhai, and R. M. Reffat. 2018. “Generating proper building envelopes for photovoltaics integration with shape grammar theory.” Energy Build. 158: 326–341. https://doi.org/10.1016/j.enbuild.2017.09.077.
Youssef, A. M. A., Z. J. Zhai, and R. M. Reffat. 2016. “Genetic algorithm based optimization for photovoltaics integrated building envelope.” Energy Build. 127: 627–636. https://doi.org/10.1016/j.enbuild.2016.06.018.
Yu, W., B. Li, H. Jia, M. Zhang, and D. Wang. 2015. “Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design.” Energy Build. 88: 135–143. https://doi.org/10.1016/j.enbuild.2014.11.063.
Zhu, L., B. Wang, and Y. Sun. 2020. “Multi-objective optimization for energy consumption, daylighting and thermal comfort performance of rural tourism buildings in north China.” Build. Environ. 176: 106841. https://doi.org/10.1016/j.buildenv.2020.106841.
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Published In
Journal of Architectural Engineering
Volume 30 • Issue 3 • September 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Mar 20, 2023
Accepted: Jan 22, 2024
Published online: May 17, 2024
Published in print: Sep 1, 2024
Discussion open until: Oct 17, 2024
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