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In-Depth Analysis of Energy Efficiency Related Factors in Commercial Buildings Using Data Cube and Association Rule Mining

Author

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  • Byeongjoon Noh

    (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea)

  • Juntae Son

    (School of Planning, Design, and Construction, Michigan State University, 552 W. Circle Dr., East Lansing, MI 48824, USA)

  • Hansaem Park

    (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea)

  • Seongju Chang

    (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea)

Abstract

Significant amounts of energy are consumed in the commercial building sector, resulting in various adverse environmental issues. To reduce energy consumption and improve energy efficiency in commercial buildings, it is necessary to develop effective methods for analyzing building energy use. In this study, we propose a data cube model combined with association rule mining for more flexible and detailed analysis of building energy consumption profiles using the Commercial Buildings Energy Consumption Survey (CBECS) dataset, which has accumulated over 6700 existing commercial buildings across the U.S.A. Based on the data cube model, a multidimensional commercial sector building energy analysis was performed based upon on-line analytical processing (OLAP) operations to assess the energy efficiency according to building factors with various levels of abstraction. Furthermore, the proposed analysis system provided useful information that represented a set of energy efficient combinations by applying the association rule mining method. We validated the feasibility and applicability of the proposed analysis model by structuring a building energy analysis system and applying it to different building types, weather conditions, composite materials, and heating/cooling systems of the multitude of commercial buildings classified in the CBECS dataset.

Suggested Citation

  • Byeongjoon Noh & Juntae Son & Hansaem Park & Seongju Chang, 2017. "In-Depth Analysis of Energy Efficiency Related Factors in Commercial Buildings Using Data Cube and Association Rule Mining," Sustainability, MDPI, vol. 9(11), pages 1-20, November.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:11:p:2119-:d:119321
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    References listed on IDEAS

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    Cited by:

    1. Feifeng Jiang & Kwok Kit Richard Yuen & Eric Wai Ming Lee & Jun Ma, 2020. "Analysis of Run-Off-Road Accidents by Association Rule Mining and Geographic Information System Techniques on Imbalanced Datasets," Sustainability, MDPI, vol. 12(12), pages 1-32, June.
    2. Guo, Hongshan & Ferrara, Maria & Coleman, James & Loyola, Mauricio & Meggers, Forrest, 2020. "Simulation and measurement of air temperatures and mean radiant temperatures in a radiantly heated indoor space," Energy, Elsevier, vol. 193(C).

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