• Proceedings of the Korean Society of Marine Engineers Conference
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• 2011.06a
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• pp.273-273
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• 2011

• Journal of the Korean Solar Energy Society
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• v.26 no.1
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• pp.13-19
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• 2006

The efficiency of building-integrated photovoltaic(BIPV) system is mainly determined by solar radiation and the temperature of PV modules. The performance of BIPV systems is reported to be different from that of conventional PV systems installed in the open-air. This paper presents the relationship of solar radiation and electricity generation from a 2kWp roof-integrated PV system that is applied as building elements on an experimental house, and the energy saving effect of the BIPV system for a typical house. For the performance evaluation of the BIPV system, it produced a regression equation with measured data for winter days. The regression equation showed that a comparison of the measured electricity generation and the predicted electricity generation of the BIPV system were meaningful. It showed that an annual electricity generation of the system appeared to cover around 52% of an annual electricity consumption of a typical domestic house with the floor area of $96m^2$.

• Journal of Korean Society of Environmental Engineers
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• v.34 no.9
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• pp.604-612
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• 2012

Self-management plan for GHG (Greenhouse Gas) reduction should be prepared in academic facilities, which occupy a large amount of energy consumption. In this study, a university was chosen as one of the major academic facilities and its energy consuming pattern and GHG emission were analyzed. The results have shown that annual $CO_2$ emission from university buildings was 10,452 ton-$CO_2$ (0.65 ton-$CO_2/m^2$), and dependent upon 78.0% electricity, 20.5% LNG and 1.5% oil, respectively as energy sources. According to more detail analysis by usage of energy consumption, appliances occupies 36.7% followed by gas heating (18.9%), lighting (18.6%), heating with electricity (12.5%), cooling with electricity (10.2%), transportation (1.5%), gas cooling (1.2%) and cooking (0.4%). Furthermore, annual $CO_2$ emissions per unit area and a student by electricity usage were evaluated to 51.30 kg-$CO_2/m^2$ and 981.86 kg-$CO_2$/capita, respectively and those by LNG usage were 14.61 kg-$CO_2/m^2$ and 241.01 kg-$CO_2$/capita.

• Environmental Engineering Research
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• v.21 no.4
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• pp.409-419
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• 2016

As a major energy consumption province, the issue about the carbon emissions in Hebei Province, China has been concerned by the government. The carbon emissions can be effectively reduced due to a more rational energy consumption structure. Thus, in this paper the constraint of low carbon emissions is considered as a foundation and four energies--coal, petroleum, natural gas and electricity including wind power, nuclear power and hydro-power etc are selected as the main analysis objects of the adjustment of energy structure. This paper takes energy cost minimum and carbon trading cost minimum as the objective functions based on the economic growth, energy saving and emission reduction targets and constructs an optimization model of energy consumption structure. And empirical research about energy consumption structure optimization in 2015 and 2020 is carried out based on the energy consumption data in Hebei Province, China during the period 1995-2013, which indicates that the energy consumption in Hebei dominated by coal cannot be replaced in the next seven years, from 2014 to 2020, when the coal consumption proportion is still up to 85.93%. Finally, the corresponding policy suggestions are put forward, according to the results of the energy structure optimization in Hebei Province.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.5
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• pp.414-424
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• 2015

In this research, we consider a smart grid network of electricity with multiple consumers connected to a monopolistic provider. Each consumer can be informed the real time price changes through the smart meter and updates his consumption schedule to minimize the energy consumption expenditures by which the required power demand should be satisfied under the given real time pricing scheme. This real-time decision making problem has been recently studied through game-theoretic approach. The present paper contributes to the existing literature by incorporating storage appliance into the set of available household appliances which has somewhat distinctive functions compared to other types of appliances and would be regarded to play a significant role in energy consumption scheduling for the future smart grid. We propose a game-theoretic algorithm which could draw the optimal energy consumption scheduling for each household appliances including storage. Results on simulation data showed that the storage contributed to increase the efficiency of energy consumption pattern in the viewpoint of not only individual consumer but also whole system.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.5
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• pp.207-217
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• 2018

Recently, the importance of energy demand management, particularly peak load control, has been increasing due to the policy changes of the Second Energy Basic Plan. Even though the installation of distributed generation systems such as Photovoltaic and energy storage systems (ESS) are encouraged, high initial installation costs make it difficult to expand their supply. In this study, the power consumption of a university building was measured in real time and the measured power consumption data was used to calculate the optimal installation capacity of the Photovoltaic and ESS, respectively. In order to calculate the optimal capacity, it is necessary to analyze the operation methods of the Photovoltaic and ESS while considering the KEPCO electricity billing system, power consumption patterns of the building, installation costs of the Photovoltaic and ESS, estimated savings on electric charges, and life time. In this study, the power consumption of the university building with a daily power consumption of approximately 200kWh and a peak power of approximately 20kW was measured per minute. An economic analysis conducted using these measured data showed that the optimal capacity was approximately 30kW for Photovoltaic and approximately 7kWh for ESS.

• Journal of the Korean Solar Energy Society
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• v.29 no.5
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• pp.8-13
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• 2009

Feasibilities of the application of a micro gas turbine cogeneration system to a large size hospital building are studied by estimating energy demands and supplies. The energy demand for electricity is estimated by surveying and sorting the consumption records for various equipment and devices. The cooling heating, and hot water demands are further refined with TRNSYS and ESP-r to generate load profiles for the subsequent operation simulations. The operation of the suggested cogeneration system in conjunction with the load data is simulated for a time span of a year to predict energy consumption and gain profile. The simulation revealed that the thermal efficiency of the gas turbine is about 30% and it supplies 60% of the electricity required by the building. The recovered heat can meet 56% of total heating load and 67% of cooling, and the combined efficiency reaches up to 70%.

• Environmental and Resource Economics Review
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• v.17 no.1
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• pp.23-49
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• 2008

In this paper, we estimate whether changes in the economic environment a firm operates in affects technical efficiency. Since efficiency gains can manifest themselves in a variety of ways, we estimate for efficiency gains on annual generating plants input demand for expenses and fuel use. We find that generating plants reduced their workers and costs respectively by 6.8% in 1999 and 37.9% in 2001. However, they didn't show any systematic caloric consumption changes facing various economic environmental shocks. These results suggest that restructuring brought efficiency gains by replacing regulated monopoly with a more competitive firms in the context of the Korean electricity market.

• Journal of Advanced Technology Convergence
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• v.2 no.4
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• pp.43-48
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• 2023

Globally, there is a collaborative effort to achieve global carbon neutrality in response to climate change. In the case of South Korea, greenhouse gas emissions are rapidly increasing, presenting an urgent situation that requires resolution. In this context, this study developed a thermal energy collection device named a 'steam trap' and created an AI model capable of predicting future electricity usage by collecting energy usage data through steam traps. The average accuracy of electricity usage prediction with this AI model was 96.7%, demonstrating high precision. Consequently, the AI model enables the prediction and management of days with high electricity consumption and identifies which facilities contribute to elevated power usage. Future research aims to optimize energy consumption efficiency through efficient equipment operation using anomaly detection in steam traps and standardizing energy management systems, with the ultimate goal of reducing greenhouse gas emissions.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.66-73
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• 2011

With rapid industrialization, electric power consumption has been increasing every year in Korea and in other countries. The provision of additional power stations to produce more electricity is one possible response to this steady increase in consumption. Another alternative is to improve the efficiency of existing stations through timely and effective maintenance works. Since the construction of a new power plant involves a large amount of capital as well as difficulties in the selection of sites, the improvement of existing power stations' efficiencies is often a preferred solution. Therefore, this paper focuses on maintenance project management to address this issue. Among several types of maintenance works, this paper concentrates on planned outage maintenance (POM). This focus arises from the critical impact of POM on other maintenance work, as well as the entire process of electricity production. Unlike other maintenance work, POM is done on the basis of mid and long-term plans. In addition, only POM works are conducted during the outage of all power plants. To evaluate the efficiency of POM works, budget data relating to 164 POM projects between 2001 and 2008 was collected from 7 sites in South Korea. Data envelopment analysis (DEA) was selected as a methodology for evaluation. From this analysis, comparative study was used to determine the categories of projects that had performed well, and those with the most optimized budget structures. Moreover, through discussion with industry, this research develops a feasible proposed method by which to enhance the efficiency of POM projects.