Historical residential electricity data and natural gas consumption data were collected for, respectively, 1,200 and 178 residences in a small town in the USA. These data were merged with local building and weather databases, and energy consumption models were developed for each residence, revealing substantial variation in heating and cooling intensity. After estimating approximate physical building characteristics, energy profiles for each residence were calculated, and savings from adoption of the most cost-effective energy-efficiency measures for each residence were estimated. Effectively, we wish to leverage commonly available data sets to infer characteristics of building envelopes and equipment, without the need for detailed on-site audits. This study evaluates the potential energy savings for the residences studied and, by extrapolation, for the entire town, as a function of cost if the savings measures were to be implemented in rank-order of cost effectiveness to show that savings penetration for the community comes with nonlinearly increasing cost. The results show that nearly a 32 % collective savings in HVAC energy use could be achieved with a collective levelized cost for energy-saving measures of $10/mmBTU saved if the most cost-effective measures among the entire community are implemented first.
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Based on the experience of the authors’ involvement with the Building Energy Center at the University of Dayton (http://www.udayton.edu/engineering/building_energy/index.php).
Allcott, H., & Greenstone, M. (2012). Is there an energy efficiency gap? Journal of Economic Perspectives, 26(1), 3–28.
Arimura, T., Li, S., Newell, R. G., & Palmer, K. (2012). Cost-effectiveness of electricity energy efficiency programs. The Energy Journal, 33(2), 63–99.
Brecha, R. J., Mitchell, A., Hallinan, K., & Kissock, K. (2011). Prioritizing investment in residential energy efficiency and renewable energy—a case study for the U.S. Midwest. Energy Policy, 39(5), 2982–2992. doi:10.1016/j.enpol.2011.03.011.
Brown, M., Gumerman, E., Sun, X., Baek, Y., Wang, J., Cortes, R., Soumonni, D., (2010). “Energy efficiency in the south”. Atlanta, GA: Southeast Energy Efficiency Alliance. http://nicholasinstitute.duke.edu/sites/default/files/publications/energy-efficiency-in-the-south-paper.pdf.
Dietz, T., Gardner, G. T., Gilligan, J., Stern, P. C., & Vandenbergh, M. P. (2009). Household actions can provide a behavioral wedge to rapidly reduce US Carbon Emissions. Proceedings of the National Academy of Sciences, 106(44), 18452–18456. doi:10.1073/pnas.0908738106.
DoE–EERE (2010). “Building America”. Building America Kick-off Presentation. http://apps1.eere.energy.gov/buildings/publications/pdfs/building_america/ns/plenary_1_doe_initiatives.pdf.
DoE–EERE (2013). “On-bill repayment programs.” http://www1.eere.energy.gov/wip/solutioncenter/onbillrepayment.html.
EIA (2009). “Residential Energy Consumption Survey (RECS)”. Energy Information Administration. http://www.eia.gov/consumption/residential.
EIA. (2013). “Annual energy outlook 2013”. Government agency. Washington: Energy Information Administration.
Gillingham, K., Newell, R., & Palmer, K. (2006). Energy efficiency policies: a retrospective examination. Annual Review of Environment and Resources, 31(1), 161–192. doi:10.1146/annurev.energy.31.020105.100157.
Granade, H. C., Creyts, J., Derkach, A., Farese, P., Nyquist, S., Ostroski, K. (2009). “Unlocking energy efficiency in the U.S. Economy”. McKinsey Global Energy and Materials.
Hallinan, K. P., Mitchell, A., Brecha, R. L., & Kissock, J. K. (2011). Targeting residential energy reduction for city utilities using historical electrical utility data and readily available building data. ASHRAE Transactions, 117(2), 577.
Koomey, J., Webber, C., Atkinson, C., & Nicholls, A. (2001). Addressing energy-related challenges for the US buildings sector: results from the clean energy futures study. Energy Policy, 29, 1209–1221.
Lammers, N., Sever, F., Abels, B., Kissock, K. (2011). “Measuring progress with normalized energy intensity.” In Detroit: SAE.
NOAA (2013). “Climate data online”. Database. National Oceanic and Atmospheric Administration. http://www.ncdc.noaa.gov/cdo-web.
NREL (2005). “Typical meteorological year 3”. Database. National Solar Radiation Data Base. National Renewable Energy Laboratory. http://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/tmy3/.
NREL (2010). “National residential efficiency measures database”. Database. http://www.nrel.gov/ap/retrofits/.
Rohmund, I., Wikler, G., Faruqui, A., Siddiqui, O., Tempchin, R. (2008). “Assessment of achievable potential for energy efficiency and demand response in the U.S. (2010–2030).” In, 5–261–5–272. ACEEE.
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Villoria-Siegert, R., Brodrick, P., Hallinan, K. et al. Cost-availability curves for hierarchical implementation of residential energy-efficiency measures. Energy Efficiency 8, 267–279 (2015). https://doi.org/10.1007/s12053-014-9291-5
- Levelized cost
- Energy efficiency
- Utility programs