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Simulated building energy demand biases resulting from the use of representative weather stations

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  • Burleyson, Casey D.
  • Voisin, Nathalie
  • Taylor, Z. Todd
  • Xie, Yulong
  • Kraucunas, Ian

Abstract

Numerical building models are typically forced with weather data from a limited number of “representative cities” or weather stations representing different climate regions. The use of representative weather stations reduces computational costs, but often fails to capture spatial heterogeneity in weather that may be important for simulations aimed at understanding how building stocks respond to a changing climate. We quantify the potential reduction in temperature and load biases from using an increasing number of weather stations over the western U.S. Our novel approach is based on deriving temperature and load time series using incrementally more weather stations, ranging from 8 to roughly 150, to evaluate the ability to capture weather patterns across different seasons. Using 8 stations across the western U.S., one from each IECC climate zone, results in an average absolute summertime temperature bias of ∼4.0°C with respect to a high-resolution gridded dataset. The mean absolute bias drops to ∼1.5°C using all available weather stations. Temperature biases of this magnitude could translate to absolute summertime mean simulated load biases as high as 13.5%. Increasing the size of the domain over which biases are calculated reduces their magnitude as positive and negative biases may cancel out. Using 8 representative weather stations can lead to a 20–40% bias of peak building loads during both summer and winter, a significant error for capacity expansion planners who may use these types of simulations. Using weather stations close to population centers reduces both mean and peak load biases. This approach could be used by others designing aggregate building simulations to understand the sensitivity to their choice of weather stations used to drive the models.

Suggested Citation

  • Burleyson, Casey D. & Voisin, Nathalie & Taylor, Z. Todd & Xie, Yulong & Kraucunas, Ian, 2018. "Simulated building energy demand biases resulting from the use of representative weather stations," Applied Energy, Elsevier, vol. 209(C), pages 516-528.
  • Handle: RePEc:eee:appene:v:209:y:2018:i:c:p:516-528
    DOI: 10.1016/j.apenergy.2017.08.244
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    References listed on IDEAS

    as
    1. Spandagos, Constantinos & Ng, Tze Ling, 2017. "Equivalent full-load hours for assessing climate change impact on building cooling and heating energy consumption in large Asian cities," Applied Energy, Elsevier, vol. 189(C), pages 352-368.
    2. Huang, Jianhua & Gurney, Kevin Robert, 2016. "The variation of climate change impact on building energy consumption to building type and spatiotemporal scale," Energy, Elsevier, vol. 111(C), pages 137-153.
    3. Hong, Tianzhen & Chang, Wen-Kuei & Lin, Hung-Wen, 2013. "A fresh look at weather impact on peak electricity demand and energy use of buildings using 30-year actual weather data," Applied Energy, Elsevier, vol. 111(C), pages 333-350.
    4. Zhou, Yuyu & Clarke, Leon & Eom, Jiyong & Kyle, Page & Patel, Pralit & Kim, Son H. & Dirks, James & Jensen, Erik & Liu, Ying & Rice, Jennie & Schmidt, Laurel & Seiple, Timothy, 2014. "Modeling the effect of climate change on U.S. state-level buildings energy demands in an integrated assessment framework," Applied Energy, Elsevier, vol. 113(C), pages 1077-1088.
    5. Chung, Mo & Park, Hwa-Choon, 2010. "Development of a software package for community energy system assessment – Part I: Building a load estimator," Energy, Elsevier, vol. 35(7), pages 2767-2776.
    6. Xu, Peng & Huang, Yu Joe & Miller, Norman & Schlegel, Nicole & Shen, Pengyuan, 2012. "Impacts of climate change on building heating and cooling energy patterns in California," Energy, Elsevier, vol. 44(1), pages 792-804.
    7. Wan, Kevin K.W. & Li, Danny H.W. & Pan, Wenyan & Lam, Joseph C., 2012. "Impact of climate change on building energy use in different climate zones and mitigation and adaptation implications," Applied Energy, Elsevier, vol. 97(C), pages 274-282.
    8. Sheridan, Blaise & Baker, Scott D. & Pearre, Nathaniel S. & Firestone, Jeremy & Kempton, Willett, 2012. "Calculating the offshore wind power resource: Robust assessment methods applied to the U.S. Atlantic Coast," Renewable Energy, Elsevier, vol. 43(C), pages 224-233.
    9. Jianhua Huang & Kevin Robert Gurney, 2016. "Impact of climate change on U.S. building energy demand: sensitivity to spatiotemporal scales, balance point temperature, and population distribution," Climatic Change, Springer, vol. 137(1), pages 171-185, July.
    10. Sailor, David J, 2001. "Relating residential and commercial sector electricity loads to climate—evaluating state level sensitivities and vulnerabilities," Energy, Elsevier, vol. 26(7), pages 645-657.
    11. Dirks, James A. & Gorrissen, Willy J. & Hathaway, John H. & Skorski, Daniel C. & Scott, Michael J. & Pulsipher, Trenton C. & Huang, Maoyi & Liu, Ying & Rice, Jennie S., 2015. "Impacts of climate change on energy consumption and peak demand in buildings: A detailed regional approach," Energy, Elsevier, vol. 79(C), pages 20-32.
    12. Shimoda, Yoshiyuki & Asahi, Takahiro & Taniguchi, Ayako & Mizuno, Minoru, 2007. "Evaluation of city-scale impact of residential energy conservation measures using the detailed end-use simulation model," Energy, Elsevier, vol. 32(9), pages 1617-1633.
    13. Mirasgedis, S. & Sarafidis, Y. & Georgopoulou, E. & Lalas, D.P. & Moschovits, M. & Karagiannis, F. & Papakonstantinou, D., 2006. "Models for mid-term electricity demand forecasting incorporating weather influences," Energy, Elsevier, vol. 31(2), pages 208-227.
    14. Li, Danny H.W. & Yang, Liu & Lam, Joseph C., 2012. "Impact of climate change on energy use in the built environment in different climate zones – A review," Energy, Elsevier, vol. 42(1), pages 103-112.
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