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

B. Noh; J. Son; H. Park; S. Chang

International · International Journal · 2017

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

Authors B. Noh , J. Son, H. Park, and S. Chang

Venue Sustainability , 9(11), 2017.

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AI-ready brief

: 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.

Author 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.

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Citation-ready BibTeX

@article{noh2017indepthanalysisofenergye,
  title   = {In-Depth Analysis of Energy Efficiency Related Factors in Commercial Buildings Using Data Cube and Association Rule Mining},
  author  = {B. Noh and J. Son and H. Park and S. Chang},
  year    = {2017},
  journal = {Sustainability , 9(11), 2017.}
}

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