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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PurposeThe purpose of this study is to examine the energy savings in the indoor environment, using strategies that adopt the characteristics of nature, called biomimetic solutions. This research designed a biomimetic window system to bring daylight into interior spaces in educational buildings where daylight cannot be reached. Specifically, this study assessed how the daylight that was achieved via a biomimetic window system would affect energy savings using an energy simulation method.Design/methodology/approachThis study explored how biomimetic methods would affect the building environment and which biomimetic method would involve the building's energy saving with daylight. The research intended to develop a novel biomimetic window system that can bring daylight to the basement floor of an existing building on a university campus to find out how much the biomimetic window system would affect the energy savings of the building. Referring to the existing building's layout and structure, energy simulation models were developed, and the energy consumptions were estimated.FindingsSimulation models proved that the biomimetic window system has sufficient performance to bring more daylight to the basement floor of the building. Furthermore, it was confirmed that the use of the biomimetic window system for the building could reduce energy usage compared to the actual energy usage of the current building without biomimetic windows.Research limitations/implicationsFirst, this study was adopted as a computer-designed simulation method instead of using a real-world system. Although this study designed the biomimetic window system based on previous studies, it should be considered the possibility of other problems when the system is actually built in. Second, it is necessary to predict how much an initial budget is required when the system is built. It means that this study did not calculate the lifecycle cost of the biomimetic window system. It will also be necessary to compare energy consumption to the required initial budget. Lastly, this study was simulated based on weather data in cold regions, and it did not compare/analyze different climate regions. Different results may be predicted if the biomimetic window system is built in different climatic regions.Originality/valueThis research showed new practical ways to capture and transmit solar heat and light using a biomimetic solution. Furthermore, using the proposed novel biomimetic window system, the amount of energy reduction can be calculated, and this method could be applied in the interior non-window spaces of academic and related types of buildings.

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.

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