Technical Paper
Jan 20, 2016

Optimal Trade-Offs between Housing Cost and Environmental Performance

Publication: Journal of Architectural Engineering
Volume 22, Issue 2

Abstract

Housing units in the United States account for 18% of the total greenhouse gas emissions and 58% of public-supply water consumption. These negative environmental impacts of housing units can be reduced by integrating green building measures and fixtures, such as geothermal heat pumps and water-saving plumbing fixtures. The integration of these green measures often leads to an increase in the initial costs of the housing units, and therefore decision makers need to study and optimize the potential trade-offs between the environmental performance of housing units and their initial costs. This paper presents a multiobjective optimization model that is capable of optimizing housing design and construction decisions to generate optimal trade-offs between maximizing the environmental performance of housing units and minimizing their initial costs. The model was designed to maximize housing environmental performance during its operational phase by reducing its greenhouse gas emissions and water consumption. The model was developed in two main phases that formulated and implemented the model with the use of multiobjective genetic algorithms to provide the capability of generating Pareto optimal trade-offs between housing cost and environmental performance. Analysis of an application example is presented to demonstrate the use of the model and its capabilities in identifying Pareto optimal configurations of housing design and construction decisions. The primary contributions of this research to the body of knowledge include its comprehensive set of metrics and novel methodology that can be used to measure and quantify the impact of the design and construction decisions of housing units on their environmental performance, including greenhouse gas emissions and water consumption, and its novel multiobjective optimization methodology that is capable of generating Pareto optimal trade-offs between the environmental performance of housing units and their initial costs.

Get full access to this article

View all available purchase options and get full access to this article.

References

Asadi, E., da Silva, M. G., Antunes, C. H., and Dias, L. (2012a). “Multi-objective optimization for building retrofit strategies: A model and an application.” Energy Build., 44, 81–87.
Asadi, E., da Silva, M. G., Antunes, C. H., and Dias, L. (2012b). “A multi-objective optimization model for building retrofit strategies using TRNSYS simulations, GenOpt and MATLAB.” Build. Environ., 56, 370–378.
ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers). (2006). “ASHRAE greenguide—The design, construction, and operation of sustainable buildings.” 〈http://www.knovel.com/knovel2/Toc.jsp?BookID=2242〉 (Feb. 20, 2014).
ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers). (2011). 62.2 user's manual: ANSI/ASHRAE standard 62.2–2010, ventilation and acceptable indoor air quality in low-rise residential buildings, Atlanta.
Bichiou, Y., and Krarti, M. (2011). “Optimization of envelope and HVAC systems selection for residential buildings.” Energy Build., 43(12), 3373–3382.
Caldas, L. G., and Norford, L. K. (2003). “Genetic algorithms for optimization of building envelopes and the design and control of HVAC systems.” J. Sol. Energy Eng., 125(3), 343–351.
Chantrelle, F. P., Lahmidi, H., Keilholz, W., Mankibi, M. E., and Michel, P. (2011). “Development of a multicriteria tool for optimizing the renovation of buildings.” Appl. Energy, 88(4), 1386–1394.
Charalambides, J., and Wright, J. (2013). “Effect of early solar energy gain according to building size, building openings, aspect ratio, solar azimuth, and latitude.” J. Archit. Eng., 209–216.
Christensen, C., Anderson, R., Horowitz, S., Courtney, A., and Spencer, J. (2006). “BEopt software for building energy optimization: Features and capabilities.” NREL/TP-550-39929, National Renewable Energy Laboratory, Golden, CO.
Congradac, V., and Kulic, F. (2009). “HVAC system optimization with CO2 concentration control using genetic algorithms.” Energy Build., 41(5), 571–577.
Deb, K., Agrawal, S., Pratap, A., and Meyarivan, T. (2000). “A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II.” Lect. Notes Comput. Sci., 1917, 849–858.
Dorer, V., and Weber, A. (2009). “Energy and CO2 emissions performance assessment of residential micro-cogeneration systems with dynamic whole-building simulation programs.” Energy Convers. Manage., 50(3), 648–657.
EIA (Energy Information Administration) and DOE. (2011). Annual energy review 2010, U.S. Government Printing Office, Washington, DC.
Energy Star. (2012). “Lighting.” 〈http://www.energystar.gov/index.cfm?c=lighting.pr_lighting_landing〉 (Sept. 3, 2015).
EnergyPlus 8.0 [Computer software]. DOE, Washington, DC. 〈http://apps1.eere.energy.gov/buildings/energyplus/〉 (Sept. 3, 2015).
EPA (Environmental Protection Agency). (2013). “Indoor airPLUS construction specifications.” 〈http://www.epa.gov/indoorairplus/pdfs/construction_specifications.pdf〉 (May 28, 2014).
EPA (Environmental Protection Agency). (2014). “What is WaterSense?” 〈http://www.epa.gov/watersense/about_us/what_is_ws.html〉 (Sept. 3, 2015).
EPA (Environmental Protection Agency) and DOE. (2014). “A green home begins with Energy Star Blue.” 〈http://www.energystar.gov/index.cfm?c=new_homes.nh_greenbuilding〉 (Sept. 3, 2015).
EPAOAQ (Environmental Protection Agency Office of Air Quality Planning and Standards). (1996). Supplement B to compilation of air pollutant emission factors, Vol. 1, U.S. EPA, Office of Air and Radiation, Office of Air Quality Planning and Standards, Research Triangle Park, NC.
EPAOAQ (Environmental Protection Agency Office of Air Quality Planning and Standards). (1999). Vol. 1, AP-42, Stationary point and area, 5th Ed., U.S. EPA, Office of Air and Radiation, Office of Air Quality Planning and Standards, Research Triangle Park, NC.
Ernest Orlando Berkeley National Laboratory. (2012). EnergyPlus engineering reference, Univ. of California, Berkeley, CA.
Fazlollahi, S., Mandel, P., Becker, G., and Maréchal, F. (2012). “Methods for multi-objective investment and operating optimization of complex energy systems.” Energy, 45(1), 12–22.
Fesanghary, M., Asadi, S., and Geem, Z. W. (2012). “Design of low-emission and energy-efficient residential buildings using a multi-objective optimization algorithm.” Build. Environ., 49, 245–250.
Fong, K. F., Hanby, V. I., and Chow, T. T. (2006). “HVAC system optimization for energy management by evolutionary programming.” Energy Build., 38(3), 220–231.
Fuller, S. K., Petersen, S. R., and Ruegg, R. T. (1996). “Life-cycle costing manual for the Federal Energy Management Program.” 〈https://www.wbdg.org/ccb/NIST/hdbk_135.pdf〉 (Nov. 17, 2013).
Hamdy, M., Hasan, A., and Siren, K. (2011). “Applying a multi-objective optimization approach for design of low-emission cost-effective dwellings.” Build. Environ., 46(1), 109–123.
Hendron, R., and Engebrecht, C. (2010). Building America house simulation protocols, National Renewable Energy Laboratory, Golden, CO, 63.
Homer TCL. (2014). “Home Depot cost search.” 〈http://www.homedepot.com/〉 (March 16, 2014).
Horowitz, S., Christensen, C., Brandemuehl, M., and Krarti, M. (2008). “Enhanced sequential search methodology for identifying cost-optimal building pathways.” NREL/CP-55043238, National Renewable Energy Laboratory, Golden, CO.
Ihm, P., and Krarti, M. (2012). “Design optimization of energy efficient residential buildings in Tunisia.” Build. Environ., 58, 81–90.
Kenny, J. F., Barber, L. B., Hutson, S. S., Linsey, K. S., K., L. J., and Maupin, M. A. (2009). Estimated use of water in the United States in 2005, USGS, Reston, VA.
Krarti, M., Erickson, P. M., and Hillman, T. C. (2005). “A simplified method to estimate energy savings of artificial lighting use from daylighting.” Build. Environ., 40(6), 747–754.
Lu, L., Cai, W., Xie, L., Li, S., and Soh, Y. C. (2005). “HVAC system optimization—In-building section.” Energy Build., 37(1), 11–22.
Magnier, L., and Haghighat, F. (2010). “Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network.” Build. Environ., 45(3), 739–746.
MATLAB [Computer software]. Mathworks, Natick, MA.
Rock, B. A. (2009). “Thermal and economic evaluation of slab-on-grade insulation in wood-framed buildings.” J. Archit. Eng., 14–25.
RSMeans. (2012). RSMeans building construction cost data 2013, Norwell, MA.
Rushing, A. S., Kneifel, J. D., and Lippiatt, B. C. (2010). “Energy price indices and discount factors for life-cycle cost analysis—April 2010: Annual supplement to NIST handbook 135 and NBS special publication 709.” NISTIR-85-3273-25, National Institute of Standards and Technology, Gaithersburg, MD.
Tuhus-Dubrow, D., and Krarti, M. (2010). “Genetic-algorithm based approach to optimize building envelope design for residential buildings.” Build. Environ., 45(7), 1574–1581.
U.S. Census Bureau. (2000). “Historical census of housing tables.” 〈http://www.census.gov/hhes/www/housing/census/historic/units.html〉. (Sept. 3, 2015).
U.S. Office of Energy Markets and EIA (Energy Information Administration). (1990). Emissions of greenhouse gases in the United States, U.S. Government Printing Office, Washington, DC.
Wang, W., Zmeureanu, R., and Rivard, H. (2005). “Applying multi-objective genetic algorithms in green building design optimization.” Build. Environ., 40(11), 1512–1525.
Wetter, M. (2001). “GenOpt—A generic optimization program.” 7th Int. IBPSA Conf., International Building Performance Simulation Association.
Wright, J. A., Brownlee, A., Mourshed, M. M., and Wang, M. (2014). “Multi-objective optimization of cellular fenestration by an evolutionary algorithm.” J. Build. Perform. Simul., 7(1), 33–51.
Wu, W., Simpson, A., and Maier, H. (2010). “Accounting for greenhouse gas emissions in multiobjective genetic algorithm optimization of water distribution systems.” J. Water Resour. Plann. Manage., 146–155.
Yao, J. (2014). “A multi-objective (energy, economic and environmental performance) life cycle analysis for better building design.” Sustainability, 6(2), 602–614.
Zitzler, E., Laumanns, M., and Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm, Eidgenössische Technische Hochschule Zürich (ETH), Institut für Technische Informatik und Kommunikationsnetze (TIK), Zürich, Switzerland.

Information & Authors

Information

Published In

Go to Journal of Architectural Engineering
Journal of Architectural Engineering
Volume 22Issue 2June 2016

History

Received: Sep 15, 2014
Accepted: Sep 25, 2015
Published online: Jan 20, 2016
Published in print: Jun 1, 2016
Discussion open until: Jun 20, 2016

Permissions

Request permissions for this article.

Authors

Affiliations

Aslihan Karatas, A.M.ASCE [email protected]
Assistant Professor, Dept. of Civil and Architectural Engineering, Lawrence Technological Univ., Southfield, MI 48075 (corresponding author). E-mail: [email protected]
Khaled El-Rayes, M.ASCE [email protected]
Professor, Dept. of Civil and Environmental Engineering, Univ. of Illinois at Urbana–Champaign, Champaign, IL 61801. E-mail: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share