• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2396-2402
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• 2015

Air compressor, as an essential equipment used in the factory and plant operations, accounts for around 30% of the total electricity consumption in U.S.A, thereby being proposed advanced technologies to reduce electricity consumption. When the fluctuation of the compressed airflow rate is small, the system stability is increased followed by the reduction of the electricity consumption which results in the efficient design of the energy system. In the statistical analysis, the normal distribution, log normal distribution, gamma distribution or the like are generally used to identify system characteristics. However a single distribution may not fit well the data with long tail, representing sudden air flow rate especially in extremes. In this paper, authors decouple the compressed airflow rate into two parts to present a mixture of distribution function and suggest a method to reduce the electricity consumption. This reduction stems from the fact that a general pareto distribution estimates more accurate quantile value than a gaussian distribution when an airflow rate exceeds over a large number.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.785-799
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• 2018

With the development of renewable energy sector, the importance of solar energy is continuously increasing. Solar radiation forecasting is essential to accurately solar power generation forecasting. In this paper, we used time series models (ARIMA, ARIMAX, seasonal ARIMA, seasonal ARIMAX, ARIMA GARCH, ARIMAX-GARCH, seasonal ARIMA-GARCH, seasonal ARIMAX-GARCH). We compared the performance of the models using mean absolute error and root mean square error. According to the performance of the models without exogenous variables, the Seasonal ARIMA-GARCH model showed better performance model considering the problem of heteroscedasticity. However, when the exogenous variables were considered, the ARIMAX model showed the best forecasting accuracy.

• Proceedings of the KIEE Conference
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• 2007.11b
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• pp.246-248
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• 2007

에너지안보와 기후변화에의 대응을 위한 에너지 효율향상은 다른 에너지대안에 비하여 가장 비용 효과적으로 에너지 절감을 이끌어 낼 수 있는 시책으로 에너지원의 새로운 개발과도 동일한 의미를 부여할 수 있다. 다양한 에너지 효율향상 시책 중 에너지효율향상 의무화제도(EERS)는 전력 및 천연가스를 보다 효율적으로 생산/전달/이용하기 위한 단순하면서도 시장경제에 기반한 메커니즘으로 정부가 설정한 에너지효율 개선 목표를 지역 내 전력/가스 공급업체들에게 배분하여 의무적으로 목표를 달성토록 요구하고, 미달성시 범칙금(penalty)를 부과하거나 크레딧(credit)을 통해 거래시장에서 미달목표량에 해당하는 인증서를 확보하도록 하여 국가 전체적인 에너지 효율을 개선시키는 제도이다. 본 논문에서는 현재 시행되고 있는 EERS의 개요 및 다양한 사례들의 검토를 통해 시사점을 도출하여 국내도입 필요성에 대하여 언급하고자 한다.

• The Journal of Society for e-Business Studies
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• v.24 no.4
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• pp.119-133
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• 2019

Recently, the importance prediction of photovoltaic power (PV) is considered as an essential function for scheduling adjustments, deciding on storage size, and overall planning for stable operation of PV facility systems. In particular, since most of PV power is generated in peak time, PV power prediction in a peak time is required for the PV system operators that enable to maximize revenue and sustainable electricity quantity. Moreover, Prediction of the PV power output in peak time without meteorological information such as solar radiation, cloudiness, the temperature is considered a challenging problem because it has limitations that the PV power was predicted by using predicted uncertain meteorological information in a wide range of areas in previous studies. Therefore, this paper proposes the LSTM (Long-Short Term Memory) based the PV power prediction model only using the meteorological, seasonal, and the before the obtained PV power before peak time. In this paper, the experiment results based on the proposed model using the real-world data shows the superior performance, which showed a positive impact on improving the PV power in a peak time forecast performance targeted in this study.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.229-256
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• 2023

The increase in telecommuting and household electricity demand due to the pandemic has led to significant changes in electricity demand patterns. This has led to difficulties in identifying KEPCO's PPA (power purchase agreements) and residential solar power generation and has added to the challenges of electricity demand forecasting and grid operation for power exchanges. Unlike other energy resources, electricity is difficult to store, so it is essential to maintain a balance between energy production and consumption. A shortage or overproduction of electricity can cause significant instability in the energy system, so it is necessary to manage the supply and demand of electricity effectively. Especially in the Fourth Industrial Revolution, the importance of data has increased, and problems such as large-scale fires and power outages can have a severe impact. Therefore, in the field of electricity, it is crucial to accurately predict the amount of power generation, such as renewable energy, along with the exact demand for electricity, for proper power generation management, which helps to reduce unnecessary power production and efficiently utilize energy resources. In this study, we reviewed the renewable energy generation forecasting system, its objectives, and practical applications to construct optimal aggregated power resources using data from 169 power plants provided by the Ministry of Trade, Industry, and Energy, developed an aggregation algorithm considering the settlement of the forecasting system, and applied it to the analytical logic to synthesize and interpret the results. This study developed an optimal aggregation algorithm and derived an aggregation configuration (Result_Number 546) that reached 80.66% of the maximum settlement amount and identified plants that increase the settlement amount (B1783, B1729, N6002, S5044, B1782, N6006) and plants that decrease the settlement amount (S5034, S5023, S5031) when aggregating plants. This study is significant as the first study to develop an optimal aggregation algorithm using aggregated power resources as a research unit, and we expect that the results of this study can be used to improve the stability of the power system and efficiently utilize energy resources.

• Journal of Environmental Policy
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• v.8 no.4
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• pp.95-124
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• 2009

This study aims to introduce greenhouse gas emission trading in Korea as a highly cost-effective mechanism for controlling emissions. The Basic Act on Low-Carbon Green Growth will cover methods of emissions allocation, national inventory, and trading systems (i.e. emissions trading platforms, national registry,and clearing and settlement platforms). The Korean emission scheme will be based on the Korean Climate Change Act proposed by the National Assembly and Government with a cap-and-trade scheme. The national allowances will be allocated by the hybrid system. To establish the national inventory, TRADEMARKS (Telemetering System) and emissions factors are effective for greenhouse gas emissions measurement. It will likewise be effective for the national registry to be implemented via a Korean Integrated Registry, the emissions trading platform via the KRX (Korean Exchange), and the clearing and settlement platform via the KSD (Korean Securities Depository). In other words, the KRX will manage product development and marketing for Korean Carbon Financial Instruments (including commodities, futures, and options contracts) listed and admitted to trading on the KRX. All emissions trades will be standardized and cleared by the KSD.

• Environmental and Resource Economics Review
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• v.14 no.3
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• pp.729-751
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• 2005

RPS(Renewable Portfolio Standards) is an institutional device to promote use of renewable energy through market mechanism by making renewable energy to constitute a pre-announced portion of the electricity production. We measure economic impacts of the introduction of RPS to domestic electricity market at the levels of electricity market, individual industrial sectors and the economy as a whole. First, we examine the TREC(Tradable Renewable Energy Credits) market, where the credits in excess of the obligation of the renewable energy production are sold to those who have to meet the obligation through purchased credits. We then measure end-users' additional cost originating from the introduction of RPS and TREC in electricity production, and their impacts on price and supply in the retail electricity market. Next, using input-output analysis, we measure economic impacts of the changes in retail price and supply on individual industrial sectors and the economy as a whole. Among many others, we find small price effect and large GDP effect - sectoral electricity price rises at around 5%, sectoral price level rises by 0.258%, and sectoral GDP declines by 1.940% on average by the year 2011.

• Journal of the Korean Society for Railway
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• v.20 no.2
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• pp.182-189
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• 2017

Photovoltaic power generation systems make up a large part of the low carbon energy trend. The purpose of this study is to utilize PVsyst, a commercial forecasting program, to forecast research on the design stages of photovoltaic power generation for wider applications of this system in railroads and to consider prospective issues for photovoltaic power plants that are currently being operated. Given this, we will compare the forecast value of generated photovoltaic power, derived from foreign weather forecast information provided by NASA, along with information from Meteonorm, and the forecast values derived from the KMA weather information. By comparing these values with amounts actually generated by KPX, this research aims to secure propriety rights for wider application of photovoltaic power generation systems in railroads, and to contribute to low carbon energy for the new climate of the future.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.355-357
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• 2020

최근 에너지 효율의 중요성이 높아지고 에너지 공급 형태가 다변화하면서 다양한 에너지원을 효율적으로 관리할 수 있는 마이크로그리드 개념이 중요해지고 있다. 본 연구의 산업단지 마이크로그리드 열거래 플랫폼은 실증사이트의 전기 및 열에너지 모니터링 기능과 열에너지 거래 정산 기능을 가지며, 이를 위해 정확하고 안정적인 실증사이트 데이터가 필요하다. 하지만 실증사이트 데이터는 에너지 단위의 불일치, 센서 및 현장 운영상태에 따른 불안정성 등의 문제가 있어 수집 직후 열거래 플랫폼에서 활용할 수 없다. 따라서 수집된 데이터를 활용하기 위해 엔진 최대 출력량, 최대 전력 사용량 등의 변수별 특성을 고려하여 데이터 전처리 프로세스를 설계 및 적용하였다.

• Plant Journal
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• v.9 no.1
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• pp.36-42
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• 2013

In this study, repowering scenarios are analyzed and evaluated from the economical point of view on a case by case basis. Based on the result of evaluation, the IRR indicates 2.34% on single 750 MW LNG combined cycle unit, 3.56% on 500 MW sub-bituminous PC units and 2.31% on 200 MW circulating fluidized bed combustion units, resulting in not reaching 7% rate of discount rate and being concluded uneconomical. However, proposes that it is most economical and feasible to repower power plant into 750 MW LNG combined cycle unit as long as the economic feasibility can be improved and it is necessary for old anthracite power plant to be repowered than rebuilt under the circumstances of lacking power supply.