• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.06a
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• pp.81-82
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• 2010

Emerging global economic growth and increasing demand for energy supply and demand imbalance and the excessive use of fossil fuels existing the rapidly increasing greenhouse gas emissions and resource depletion of global energy crisis is deepening. Accordingly, improvement of living conditions around and through the natural ecological preservation and the need for a comfortable life for the meeting the importance of energy management and consumption are emerging. Many in the field of architecture for energy-saving measures, and conducting research and verify green building energy ratings and low energy for the initial steps that can be verified from the Energy Performance of BIM(Building Information Model) technology development and commercialization of the building energy to predict the performance objectively, leverages technology in an existing building energy performance analysis and possibilities of BIM-based green building process presented. In this study, using BIM for existing building energy performance analysis of data collected through the objective and efficient management of the energy it consumes Mapping and Management Plan is to research on.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.723-727
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• 2015

Building energy use estimation relies on building characteristics, its energy systems, occupants, and weather. Energy estimation of new buildings is considerably an easy task when compared to modeling existing buildings as they require calibration with actual data. Particularly, when energy estimation of existing building stock is warranted at a city-scale, the problem is exacerbated owing to lack of construction drawings and other engineering specifications. However, as collection of buildings and other infrastructure constitute cities, such predictions are a necessary component of developing and maintaining sustainable cities. This paper uses Artificial Neural Network techniques to predict electricity consumption for residential buildings situated in the City of Gainesville, Florida. With the use of 32,813 samples of data vectors that comprise of building floor area, built year, number of stories, and range of monthly energy consumption, this paper extends the prediction to environmental impact assessment of electricity usage at the urban-scale. Among others, one of the applications of the proposed model discussed in this paper is the study of urban scale Life Cycle Assessment, and other decisions related to creating sustainable cities.

• Journal of Energy Engineering
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• v.25 no.2
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• pp.79-85
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• 2016

This paper presents an advanced energy demand management for buildings. It is important to aggregate a various demand side resource which is controllable on demand response environment. Previous demand side algorithm for building is mostly restricted on single building. In this paper, we suggest energy demand management algorithm for many buildings. And, this paper shows the procedure to apply suggested demand management algorithm.

• Journal of IKEEE
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• v.26 no.1
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• pp.56-61
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• 2022

Various efforts have been made worldwide to respond to environmental problems such as climate change. Research on artificial intelligence (AI)-based energy management has been widely conducted as the most effective way to alleviate the climate change problem. In particular, buildings that account for more than 20% of the total energy delivered worldwide have been focused as a target for energy management using the building energy management system (BEMS). In this paper, we propose a multi-armed bandit (MAB)-based energy management algorithm that can efficiently decide the energy consumption level of the lighting system in each room of the building, while minimizing the discomfort levels of occupants of each room.

• Journal of Energy Engineering
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• v.25 no.1
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• pp.131-144
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• 2016

Damage occur frequently around the world on climate change and the main cause of greenhouse gas emissions regulation is growing. To this end, the government has built integrated management system for national building energy. The building energy information is total 6.8 million complex. Integrated management system for national building energy database are matched building registers information and energy information of the supply agencies. However, the matching process has its limitations so advanced work is in progress continuously. This study analyzed integrated management system for national building energy database quality and limitations and deduce improvement plan to increase system reliability and availability. The existing database matching average rate is 85.6%. 58.2% of the total non-matching data type has no building information. To ensure the ease of new database matching and the accuracy of the existing database matching, address standarization and building properties system are needed between building information and energy information. Also, The system construction is required to include information on other energy sources like petroleum energy which has high proportion of non-urban areas and small residential areas and renewable energy which has high potential in development and utilization.

• Journal of Energy Engineering
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• v.27 no.2
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• pp.32-48
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• 2018

Monitoring of energy use should be a priority in order to efficiently manage building energy use. Energy use in buildings can be managed by dividing them into energy sources, uses, and ZONE. By energy source, electricity, gas, fuel, and district heating are supplied to run the building's facilities. The purpose can be divided into five main applications, including cooling, heating, lighting, hot water and ventilation, but not many elevators and electric heaters that are difficult to include in the five applications are classified. ZONE Star refers to the comparison or separate management of areas for which the purpose of the building is similar or different. In addition, energy efficiency management requires control of the temperature, humidity, and people who will be measuring energy in the building, and the recent problem of fine dust should directly affect the ventilation of the building.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.87-93
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• 2013

In this research, a new methodology to perform building energy analysis using Industry Foundation Classes (IFC) standard has been studied. With the help of Archicad 14 modeling software, a 3D test model is generated and then exported to IFCXML format. A ruby code program retrieves the building information from the resulting IFCXML file using Nokogiri library. An INP file is created and gets ready for next energy analysis step. DOE 2.2 program analyzes the INP file and gives a detailed report of the energy cost of the building. Case study shows when using the IFC standard method, the Interoperability of the energy analysis is greatly improved. The main stream 3D building modeling software supports IFC standard. DOE 2.2 is able to read the INP file generated by IFC file. This means almost any 3D model created by main stream modeling software can be analyze in terms of energy cost Thus, IFC based energy analysis method has a promising future. With the development and application of IFC standard, designers can do more complex and easy-to-run energy analysis in a more efficient way.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1123-1146
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• 2023

In South Korea, there have been many studies on efficient building-energy management using renewable energy facilities in single zero-energy houses or buildings. However, such management was limited due to spatial and economic problems. To realize a smart zero-energy city, studying efficient energy integration for the entire city, not just for a single house or building, is necessary. Therefore, this study was conducted in the eco-friendly energy town of Chungbuk Innovation City. Chungbuk successfully realized energy independence by converging new and renewable energy facilities for the first time in South Korea. This study analyzes energy data collected from public buildings in that town every minute for a year. We propose a smart city building-energy management model based on the results that combine various renewable energy sources with grid power. Supervised learning can determine when it is best to sell surplus electricity, or unsupervised learning can be used if there is a particular pattern or rule for energy use. However, it is more appropriate to use reinforcement learning to maximize rewards in an environment with numerous variables that change every moment. Therefore, we propose a power distribution algorithm based on reinforcement learning that considers the sales of Energy Storage System power from surplus renewable energy. Finally, we confirm through economic analysis that a 10% saving is possible from this efficiency.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.16 no.10
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• pp.969-976
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• 2004

Building energy saving is one of the most important issues in these days. Energy saving control strategies should be developed properly to achieve the saving. One of such area we could apply is the HVAC (Heating, Ventilation and Air-Conditioning) system. Through the optimal control algorithm for building energy management system (EMS), you can not only save the cost of building energy, but also protect HVAC system components against the unexpected condition. In order to verify the effectiveness of building energy saving, field test was accomplished for several months at 'A' building. And to get the measured data, remote control was used. If the remote control is used in BAS (Building Automation System), control and monitoring can be done for all of the building systems, such as HVAC, power, lighting, security and fire-alarm etc. anywhere any time. Using the remote control, Control and monitoring is possible for the testing system without going there. As the results of field test, we could reduce $5{\sim}10\%$ of the building energy cost.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.408-417
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• 2022

Although traditionally perceived as being a visualization and asset management resource, the relatively rapid rate of improvement of computing power, coupled with the proliferation of cloud and edge computing and the IoT has seen the expanded functionality of modern Digital Twins (DTs). These technologies, when applied to buildings, are now providing users with the ability to analyse and predict their energy consumption, implement building controls and identify faults quickly and efficiently, while preserving acceptable comfort and well-being levels. Furthermore, when these building DTs are linked together to form a community DT, entirely new and novel energy management techniques, such as demand side management, demand response, flexibility and local energy markets can be unlocked and analysed in detail, creating circularity in the economy and making ordinary building occupants active participants in the energy market. Through the EU Horizon 2020 funded TwinERGY project, three different levels of DT (consumer - building - community) are being created to support the creation of local energy markets while optimising building performance for real-time occupant preferences and requirements for their building and community. The aim of this research work is to demonstrate the development of this new, interrelated, multi-level DT that can be used as a decision-making tool, helping to determine optimal scenarios simultaneously at consumer, building and community level, while enhancing and successfully supporting the community's management plan implementation.