• Journal of Energy Engineering
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• v.29 no.2
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• pp.61-67
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• 2020

ESS-based buildings are being widely studied as an effective methods for saving energy with ZEB, BEMS, and FEMS. However, in large scale buildings, there are many energy-consuming facilities, so it is necessary to identify important energy-consuming facilities to build a real-time measurement system. In addition, there are a myriad of factors that affect the dependent variable of energy use, therefore there is a limitation that effective energy management is difficult. Therefore, this study applied the measurement boundary setting methodology according to the energy supply status through due diligence for the demonstration ESS distribution logistics facility, and suggested the methodolgy for presenting priority for the construction of the measurement system. Afterwards, the impact variables that Acting as an independent variable affecting the energy consumption of the distribution and logistics facilities were categorized into intrinsic and meteorological variables. Lastly, all factors that could affect the energy consumption of the actual distribution and logistics facilities, were classified and presented as guidelines list. By applying the results of this study, it is possible to build a monitoring system at a low cost and high efficiency in a distribution and logistics facility with a complex structure. And by identifying the main independent variables for the measured energy consumption, effectively identifying trends in energy consumption and deriving saving points It is expected to be able to operate the ESS-based infrastructure.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.6
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• pp.15-22
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• 2018

The purpose of this study was to suggest specific evaluation data for heating and DHW energy consumption characteristics through analyzing energy consumption measurement data of gas boiler in Apartment Buildings with individual heating systems. To do this, it was measured both gas flow and electricity for heating and DHW respectively, and then it was analyzed not only characteristics according to energy sources; gas and electricity, but also the effect of various factors on heating and DHW energy consumption. The result of this study were as follows. It was developed the electric energy estimation model of a gas boiler through analysis on patterns by energy sources. And the effective factors for heating and DHW energy consumption were demonstrated as follows: the area for exclusive use, the number of auxiliary heating equipments, the number of occupants, and the number of sanitary fixtures.

• 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.

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
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• v.45 no.12
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• pp.56-61
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• 2016

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.7
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• pp.43-52
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• 2018

In this study, annual site and primary energy use intensities (EUIs) and CO2 emission intensities (CEIs) per area by end use were estimated based on the measurement data from June 2016 to May 2017 of 50 sample apartment units in Seoul. In addition, estimated site EUIs by end use were compared to the U.S. Residential Energy Consumption Survey (RECS) 2009 data. Site EUIs by end use were found to be in the order of heating > electric appliance > domestic hot water > cooking > lighting > cooling > air movement. In the case of primary EUIs and CEIs by end use, electric appliance was found to be the largest. As results of comparison with the RECS 2009 data, it was found that site EUIs were very similar for heating, domestic hot water and electric appliance, etc., but slightly different for cooling. The number of sample apartment units will continue to increase until 2020 (total number of samples 200) and intensities data by end use will be continuously updated through continuous collection of measurement data.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.36 no.5
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• pp.165-176
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• 2020

The purpose of this study is to develop a estimation method of energy consumption by end-use in office buildings. For this, the current status of information on building energy use was investigated, and the domestic and foreign literature on the classification of energy use in non-residential buildings and the estimation method of energy use were reviewed. In addition, the characteristics of energy consumption by end-use were analyzed with measurement data of 48 office buildings in Seoul. As results, the annual and monthly estimation method of energy consumption by end-use in office buildings using public and measurement data was presented, and the applicability of the estimation method was examined by applying to sample office buildings.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.8
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• pp.19-27
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• 2018

In this study, annual site and primary energy use intensities (EUIs) and $CO_2$ emission intensities (CEIs) per area by end use were estimated based on the measurement data from June 2016 to May 2017 of 19 sample office buildings in Seoul. In addition, the estimated site EUIs by end use were compared to the U.S. Commercial Buildings Energy Consumption Survey (CBECS) 2012 data. Average site EUIs by end use were found to be in the order of electric appliance (typical floors) > heating > cooling > lighting > air movement > domestic hot water > vertical transportation > city water supply. In the case of primary EUIs and CEIs by end use, electric appliance was found to be the largest. As results of comparison with the CBECS 2012 data, it was found that the site EUIs were similar for heating, cooling, domestic hot water, and electric appliance, etc., but slightly different for lighting and air movement. The number of sample office buildings will continue to increase until 2020 (total number of samples 85) and intensities data by end use will be continuously updated through continuous collection of measurement data.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.87-92
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• 2019

This paper describes the pattern analysis for data of the factory energy consumption by using machine learning method. While usual statistical methods or approaches require specific equations to represent the physical characteristics of the plant, machine learning based approach uses historical data and calculate the result effectively. Although rule-based approach calculates energy usage with the physical equations, it is hard to identify the exact equations that represent the factory's characteristics and hidden variables affecting the results. Whereas the machine learning approach is relatively useful to find the relations quickly between the data. The factory has several components directly affecting to the electricity consumption which are machines, light, computers and indoor systems like HVAC (heating, ventilation and air conditioning). The energy loads from those components are generated in real-time and these data can be shown in time-series. The various sensors were installed in the factory to construct the database by collecting the energy usage data from the components. After preliminary statistical analysis for data mining, time-series clustering techniques are applied to extract the energy load pattern. This research can attributes to develop Factory Energy Management System (FEMS).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.41-42
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• 2021

The Green New Deal policy was established to innovate the government's energy consumption structure, establish a third basic energy plan to strengthen the global competitiveness of the energy industry, and realize a carbon neutral society due to the increased need for transition to a low-carbon economy. Waterworks such as drinking water, water purification plant, and pressurization plant analyze control factors and energy consumption status by process to improve energy management efficiency and reduce energy usage through the 4th industrial revolution. Ultimately, we want to realize net-zero water purification plant.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.9
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• pp.27-34
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• 2018

The purpose of this study is to demonstrate and analyze the function of a Smart Plug before and after it is applied on the electrical appliances by controlling standby power usage. The research measures and analyzes the amount of electrical energy used while activating the Smart Plug with two types of appliances in a university facilities. The smart plugs were applied into a Group 1 appliances (Multi-function device, computer, laptop, Air con) which completely hinder the standby power, and a Group 2 appliances (Refrigerator, cold and hot water dispenser) which does not completely hinder the standby powers due to the characteristics of the function. First, the total standby power saving of all electrical appliances (Group 1 and Group 2) using the Smart plug was measured as 4.59%. Second, the energy saving of the Group 1 products was analyzed as 26.43%. Third, the standby power saving of the air conditioners from mid October to early December was measured as 31.06%, during the seasons when air conditioning was not actively in use. The research indicates that all specified appliances did have better energy efficiency with the Smart plug regardless of the amount of energy usage.