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Spatial Model for Energy Consumption of LEED-Certified Buildings

Author

Listed:
  • Jonghoon Kim

    (Department of Construction Management, University of North Florida, Jacksonville, FL 32224, USA)

  • Soo-Young Moon

    (Living Environmental Research Center, Korea Institute of Civil Engineering and Building Technology (KICT), Seoul 411-712, Republic of Korea)

  • Daehee Jang

    (Green Building Research Center, Korea Institute of Civil Engineering and Building Technology (KICT), Seoul 411-712, Republic of Korea)

Abstract

In this research endeavor, we undertook a comprehensive examination of the factors influencing the energy consumption of LEED-certified buildings, employing both a general linear regression model and a spatial delayed regression model. Gaining a profound understanding of energy utilization patterns within LEED-certified structures can significantly contribute to advancing eco-friendly construction practices. Our investigation draws upon data from a 2010 study conducted at the University of Wisconsin—Madison (UW), encompassing various independent variables, such as temperature, that exhibit some degree of correlation with energy consumption in LEED-certified buildings. The principal objective of this study is twofold: firstly, to ascertain the significance of specific exogenous variables, notably temperature, and secondly, to explore the impact of spatial factors, such as function and location, on energy usage. Our research framework encompasses meticulous data collection and rigorous analysis, culminating in the presentation and discussion of our findings. Notably, our study unveils intriguing insights. Contrary to conventional assumptions, we discovered that the energy consumption of LEED-certified buildings does not exhibit a robust linear association with average annual temperature, the count of power plants within a 50 mile radius, or the LEED rating itself. However, our spatial regression models unveil a compelling narrative: the geographic distribution and functional diversity of distinct LEED buildings wield discernible influence over energy consumption patterns. The implications of our research resonate profoundly in the realm of LEED-certified building design and construction. Architects, builders, and stakeholders should consider the nuanced interplay of spatial variables and geographical positioning in the pursuit of optimal energy efficiency. Moreover, our findings stimulate further inquiry in this field, paving the way for future investigations aimed at refining sustainable building practices and enhancing our understanding of energy consumption within LEED-certified structures.

Suggested Citation

  • Jonghoon Kim & Soo-Young Moon & Daehee Jang, 2023. "Spatial Model for Energy Consumption of LEED-Certified Buildings," Sustainability, MDPI, vol. 15(22), pages 1-15, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:16097-:d:1283555
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    References listed on IDEAS

    as
    1. Fonseca, Jimeno A. & Nevat, Ido & Peters, Gareth W., 2020. "Quantifying the uncertain effects of climate change on building energy consumption across the United States," Applied Energy, Elsevier, vol. 277(C).
    2. Fateme Dinmohammadi & Yuxuan Han & Mahmood Shafiee, 2023. "Predicting Energy Consumption in Residential Buildings Using Advanced Machine Learning Algorithms," Energies, MDPI, vol. 16(9), pages 1-23, April.
    3. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
    4. Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
    Full references (including those not matched with items on IDEAS)

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