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

Byeongjoon Noh,Seongju Chang,Hansaem Park,Juntae Son

doi:10.3390/su9112119

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.

Highlights

Increasing amounts of energy are consumed due to the growth in energy demand, emitting a vast amount of greenhouse gas (GHG)
We validated the feasibility and applicability of the proposed system by describing the experimental results that were applied to the Commercial Buildings Energy Consumption Survey (CBECS) dataset provided by the U.S Energy Information Administration (EIA) using the Oracle database management system and R tools
We proposed a new evaluative methodology for the analysis of influencing factors that affect energy efficiency derived from selected commercial buildings in the U.S by utilizing the CBECS dataset

Summary

Introduction

Increasing amounts of energy are consumed due to the growth in energy demand, emitting a vast amount of greenhouse gas (GHG). This has caused various adverse environmental problems such as global warming and air pollution [1]. Commercial buildings are responsible for approximately 40% of the total energy usage in the U.S.A., which is more than that of the transportation or industry sectors [4]. In commercial buildings, heating and cooling are the biggest sources of major energy consumption [5]. To reduce building energy use for heating and cooling, it is essential to design buildings for maximized energy efficiency during the early design phase, considering location, architectural components, form, materials, orientation, and so on. It is necessary to analyze energy use in buildings, and to provide efficient building design

Methods

Results

Discussion

Conclusion