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Sustainability 2017, 9(11), 2119; doi:10.3390/su9112119

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

1
Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
2
School of Planning, Design, and Construction, Michigan State University, 552 W. Circle Dr., East Lansing, MI 48824, USA
*
Author to whom correspondence should be addressed.
Received: 25 September 2017 / Revised: 16 November 2017 / Accepted: 16 November 2017 / Published: 17 November 2017
(This article belongs to the Section Energy Sustainability)
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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. View Full-Text
Keywords: building energy consumption analysis; commercial building energy consumption survey; data cube model; multidimensional analysis; association rule mining building energy consumption analysis; commercial building energy consumption survey; data cube model; multidimensional analysis; association rule mining
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Noh, B.; Son, J.; Park, H.; Chang, S. In-Depth Analysis of Energy Efficiency Related Factors in Commercial Buildings Using Data Cube and Association Rule Mining. Sustainability 2017, 9, 2119.

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