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A measure to manage approach to characterizing the energy impact of residential building stocks

  • Received: 31 March 2016 Accepted: 20 May 2016 Published: 26 May 2016
  • The city of San Antonio is the seventh largest in the United States by population and the second in the state of Texas, with a population of over 1.3 million people. As one of the fastest growing cities, the San Antonio residential real estate market has expanded to meet the demands of the growing population. Managing the energy footprint of single-family houses can be enhanced by big data analysis of combined metered energy consumption and building infrastructure characteristics. This study analyzes the energy intensity of 389,160 single family detached homes and identifies energy utilization trends across various residential building stock size and vintage categories. Supported by the “measure to manage” premise, this study highlights the value of this characterization as a forecasting and planning tool for sustainable growth and a more engaged consumer.

    Citation: Afamia Elnakat, Juan D. Gomez, Martha Wright. A measure to manage approach to characterizing the energy impact of residential building stocks[J]. AIMS Energy, 2016, 4(4): 574-588. doi: 10.3934/energy.2016.4.574

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  • The city of San Antonio is the seventh largest in the United States by population and the second in the state of Texas, with a population of over 1.3 million people. As one of the fastest growing cities, the San Antonio residential real estate market has expanded to meet the demands of the growing population. Managing the energy footprint of single-family houses can be enhanced by big data analysis of combined metered energy consumption and building infrastructure characteristics. This study analyzes the energy intensity of 389,160 single family detached homes and identifies energy utilization trends across various residential building stock size and vintage categories. Supported by the “measure to manage” premise, this study highlights the value of this characterization as a forecasting and planning tool for sustainable growth and a more engaged consumer.


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    [1] Gomez J, Elnakat A, Wright M, et al. (2014) Analysis of the Energy Index as a Benchmarking Indicator of Potential Energy Savings in the San Antonio, Texas Single Family Residential Sector. Energy Efficiency 8: 577-593.
    [2] Elnakat A, Gomez JD, Roberts J, et al. (2015) Big data analysis of swimming pools’ impact on household electric intensity in San Antonio, Texas. Int J Big Data Intelligence 2: 250-261. doi: 10.1504/IJBDI.2015.072162
    [3] Elnakat A, Gomez JD (2016) The flame dilemma: A data analytics study of fireplace influence on winter energy consumption at the residential household level. Energy Rep 2: 14-20. doi: 10.1016/j.egyr.2016.01.002
    [4] Elnakat A, Gomez JD (2015) Energy Engenderment: An industrialized perspective assessing the importance of engaging women in residential energy consumption management. Energy Policy 82: 166-177. doi: 10.1016/j.enpol.2015.03.014
    [5] Elnakat A (2016) The C3 of conservation: The influence of cool, convenience and cash on residential household energy conservation. Electr J 29: 22-25.
    [6] Elnakat A, Gomez JD, Booth N (2016) A zip code study of socioeconomic, demographic, and household gendered influence on the residential energy sector. Energy Rep 2: 21-27.
    [7] U.S. Energy Information Administration (2012) Annual Energy Review 2011.
    [8] Perez-Lombard L, Ortiz J, Pout C (2008) A review on buildings energy consumption information. Energy Buildings 40: 394-398. doi: 10.1016/j.enbuild.2007.03.007
    [9] Karamjeet P (2013) Managing extreme financial risk: Strategies and tactics for going concerns. New York: Academic Press. 49-52.
    [10] Van Gorp J (2005) Using key performance indicators to manage energy cost. Paper presented at the Proceedings of the Twenty-Seventh Industrial Energy Technology Conference. New Orleans, LA. 2.
    [11] Deming EW (1986) Out of the crisis. Cambridge: MIT Press.
    [12] Mattern S (2013) Municipal energy benchmarking legislation for commercial buildings: You can’t manage what you don’t measure. Boston College Environmental Affairs Law Review 40: 487-521.
    [13] RF Code (2013) You can’t manage what you don’t measure, and you can’t measure what you don’t monitor: Save money, save power, and save equipment in the Data Center. Austin, TX: RF Code.
    [14] Williams E, Matthews S, Breton M, et al. (2006) Use of a computer-based system to measure and manage energy consumption in the Home. Proceedings of the 2006 IEEE International Symposium on Electronics and the Environment: 167-172.
    [15] Brounen D, Kok N, Quigley JM (2012) Residential energy use and conservation: Economics and demographics. European Econ Rev 56: 931-945. doi: 10.1016/j.euroecorev.2012.02.007
    [16] Wiggins M, McKenney K, Brodrick J (2009) Residential energy monitorning. ASHRAE J: 88-89.
    [17] U.S. Census Bureau (2010) State & country Quickfacts: San Antonio, TX.
    [18] City of San Antonio (2011) 2010 Demographics. Department of Planning and Community Development. City of San Antonio.
    [19] DeVol R, Bedroussian A, Klowden K, et al. (2011) Best-Performing Cities 2011: Where America’s jobs are created and sustained. Santa Monica: Milken Institute.
    [20] McDonald R (2005) The economics of green building in Canada: Highlighting seven keys to cost effective green building. Thesis, Royal Roads University.
    [21] Costa D (2011) Electricity consumption and durable housing: Understanding cohort effects. Am Econ Rev 101: 88-92. doi: 10.1257/aer.101.3.88
    [22] Energy Star (2013) Energy Star Portfolio Manager: technical reference source energy. Available from:http://energystar.gov/buildings/tools-and-resources/portfolio-manager-technical-reference-source-energy.
    [23] West S (2001) Improving the sustainable development of building stock by the implementation of energy efficient, climate control technologies. Build Environ 36: 281-289. doi: 10.1016/S0360-1323(00)00007-X
    [24] Alcott B (2005) Jevon’s Paradox. Ecol Econ 54: 9-21. doi: 10.1016/j.ecolecon.2005.03.020
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  • © 2016 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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