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Most engineers predict future building energy consumption via simulation programs in the pre-design phase. In this process, many simulation steps have to be repeated to predict building energy consumption. The authors in this article proposed another way to select optimal building materials for saving commercial building energy in the U.S. using soft computing methods. To achieve the research goal, reliable public data that is provided by the U.S. Energy Information Administration was used. The data contain numerous energyrelated characteristics of buildings including gas, electricity, types of materials, and climate conditions of 6,700 commercial buildings located in the U.S. This study utilized two methods to find out optimal building materials for saving energy. First, the Principle Component Analysis was used to determine which building characteristics among over 400 characteristics have the greatest impact on gas and electricity consumption. Second, Association Rule Mining was used to extract combinations of optimal building materials. Since a building consists of a combination of various materials, energy simulation should predict for multiple factors rather than a single factor. The use of these methods would greatly reduce resources, such as limited budget and time, during the simulation process. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]