• 한국신재생에너지학회:학술대회논문집
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• 2011.11a
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• pp.155-155
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• 2011

신재생에너지 수요가 확대됨에 따라 신재생에너지 관련 제품에 소요되는 물질에 대한 관심이 확대되고 있다. 이들 물질은 공급리스크가 존재하는 희유금속이 주를 이루고 있다. 본 연구에서는 신재생에너지 등의 high tech 기술 확대로 인한 희유금속의 수요 및 공급을 전망하고 있는 미국의 critical raw material 관리 전략을 살펴보고자 한다. 미국은 2010년 12월 미국 에너지성(DOE : Department of Energy)에서 위기 물질 전략(Critical Materials Strategy)에 관한 리포트를 공표하였다. 클린 에너지 기술 4개 분야(영구자석, 선진 전지, 태양전지 박막, 형광 물질)에서 핵심이 되는 물질(희유금속 등)의 수급 불균형이 일어날 가능성에 대해 조사를 실시하여 리스크 평가하여 단기, 중단기로 구분하여 위기물질을 선정하였다. 클린 에너지 기술 4개 분야에서 핵심이 되는 물질(네오디움, 디스프로슘, 코발트, 리튬, 랜턴, 세륨, 테룰, 인듐, 갈륨, 유로피움, 테르비움, 이트륨)의 12광종 수급을 2025년까지 전망한 결과 전체적으로 단기(2010년~2015년)보다 중기(2015년~2025년)에 공급 부족이 확대한다고 예측되었다. 단기적으로는 인듐이 약간 부족하는 것 외에 디스프로슘과 이트륨에 관해서도 공급 부족할 것으로 예측되었다. 중기적으로는 코발트(전지 기술에 사용)와 유로피움(고효율 조명용의 형광 물질에 사용) 외 대상이 된 다른 모든 물질은 공급 부족이 발생할 것으로 전망되었다. 이를 종합하여 단기적으로는 디스프리슘, 유로피움, 인듐, 테르븀, 네오디움, 이트륨 등이, 중기적으로는 디스프리슘, 유로피움, 테르븀, 네오디움, 이트륨 등이 위기물질(Critical Material)로 분석되었다. 에너지성은 위기물질을 공급원다각화, 대체물질개발, 리유즈, 리사이클링 등을 국제적 파트너와 함께 추진하여 리스크를 관리할 것이며, 2011년까지 최신정보를 구축하여 위기물질 전략을 재설정할 예정이다. 체계적인 위기물질 선정 및 관리전략 등을 참조하고, 신재생에너지기술 변화에 따른 원재료의 중요성 및 리스크 관리현황을 기초로 우리나라에 적합한 위기관리 물질 선정 및 관리가 필요할 것이다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.312-321
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• 2018

The Korean DR market proposes suppression of peak demand under reliability crisis caused a natural disaster or unexpected power plant accidents as well as saving power plant construction costs and expanding amount of reserve as utility's perspective. End-user is notified a DR event signal DR execution before one hour, and executes DR based on requested amount of load reduction. This paper proposes a DR energy management algorithm that can be scheduled the optimal operations of chiller system and ESS in the next day considering the TOU tariff and DR scheme. In this DR algorithm is divided into two scheduling's; day-ahead operation scheduling with temperature forecasting error and operation rescheduling on DR operation. In day-ahead operation scheduling, the operations of DR resources are scheduled based on the finite number of ambient temperature scenarios, which have been generated based on the historical ambient temperature data. As well as, the uncertainties in DR event including requested amount of load reduction and specified DR duration are also considered as scenarios. Also, operation rescheduling on DR operation day is proposed to ensure thermal comfort and the benefit of a COB owner. The proposed method minimizes the expected energy cost by a mixed integer linear programming (MILP).

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.703-720
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• 2019

The influence of temperature on electricity demand is increasing due to extreme weather and climate change, and the climate impacts involves nonlinearity, asymmetry and complexity. Considering changes in government energy policy and the development of the fourth industrial revolution, it is important to assess the climate effect more accurately for stable management of electricity supply and demand. This study aims to analyze the effect of temperature change on electricity demand using the partial linear model. The main results obtained using the time-unit high frequency data for meteorological variables and electricity consumption are as follows. Estimation results show that the relationship between temperature change and electricity demand involves complexity, nonlinearity and asymmetry, which reflects the nonlinear effect of extreme weather. The prediction accuracy of in-sample and out-of-sample electricity forecasting using the partial linear model evidences better predictive accuracy than the conventional model based on the heating and cooling degree days. Diebold-Mariano test confirms significance of the predictive accuracy of the partial linear model.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.12
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• pp.419-430
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• 2020

This paper investigated methods to improve the forecasting accuracy of the electricity consumption prediction model. Currently, the demand for electricity has continuously been rising more than ever. Since the industrial sector uses more electricity than any other sectors, the importance of a more precise forecasting model for manufacturing sites has been highlighted to lower the excess energy production. We propose a double encoder-decoder model, which uses two separate encoders and one decoder, in order to adapt both long-term and short-term data for better forecasts. We evaluated our proposed model on our electricity power consumption dataset, which was collected in a manufacturing site of Sehong from January 1st, 2019 to June 30th, 2019 with 1 minute time interval. From the experiment, the double encoder-decoder model marked about 10% reduction in mean absolute error percentage compared to a conventional encoder-decoder model. This result indicates that the proposed model forecasts electricity consumption more accurately on manufacturing sites compared to an encoder-decoder model.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2005.05a
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• pp.217-233
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• 2005

As the Kyoto Protocol will come into effect starting February 2005, 55 member countries of the Conference of Parties of the Framework Convention on Climate Change (FCCC) will be under obligation to reduce the emissions of Carbon Dioxide $(CO_2)$ by 5.2 Percent from the 1990 levels before the year 2012. Hence the development of technology to prepare for this has been accelerated in Korea. The effect of technology varies with market size of technology, and it is necessary to control technology development period, according to the size and trend of technology market. Moreover it is essential that market analysis be finished before technology development, because market on the $(CO_2)$ Reduction and Sequestration Technology expands internationally. For that reason, it is needed to analyze domestic market and to consider technology development strategy according to analysis results. In this paper, we analyzed the domestic industry and forecasted the market size, both related to the Reduction and Sequestration Technology on $(CO_2)$ emission, which is the major component of global Green House Gas(GHG).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.3
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• pp.404-410
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• 2016

This paper proposed a program of an energy storage system(ESS) for peak shaving of high-speed railway substations The peak shaving saves cost of equipment and demand cost of the substation. To reduce the peak load, it is very important to know when the peak load appears. The past data based load profile forecasting method is easy and applicable to customers which have relatively fixed load profiles. And an optimal scheduling method of the ESS is helpful in reducing the electricity tariff and shaving the peak load efficiently. Based on these techniques, MS. NET based peak shaving program is developed. In case study, a specific daily load profile of the local substation was applied and simulated to verify performance of the proposed program.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2701-2717
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• 2023

In this paper, a hybrid fuzzy-based method is suggested for determining India's best system for power generation. This suggested approach was created using a fuzzy-based combination of the Giza Pyramids Construction (GPC) and Recalling-Enhanced Recurrent Neural Network (RERNN). GPC is a meta-heuristic algorithm that deals with solutions for many groups of problems, whereas RERNN has selective memory properties. The evaluation of the current load requirements and production profile information system is the main objective of the suggested method. The Central Electricity Authority database, the Indian National Load Dispatch Centre, regional load dispatching centers, and annual reports of India were some of the sources used to compile the data regarding profiles of electricity loads, capacity factors, power plant generation, and transmission limits. The RERNN approach makes advantage of the ability to analyze the ideal power generation from energy data, however the optimization of RERNN factor necessitates the employment of a GPC technique. The proposed method was tested using MATLAB, and the findings indicate that it is effective in terms of accuracy, feasibility, and computing efficiency. The suggested hybrid system outperformed conventional models, achieving the top result of 93% accuracy with a shorter computation time of 6814 seconds.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1248-1252
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• 1999

To provide electricity power of good quality, it is essential to establish generation of electricity plan in electric power system based on accurate power-demand prediction and cope with changes of power-need fluctuating constantly. The role of hydraulic-power generation of electricity in electric power system is of importance because responding to electric power-demand counts or reservoir-type hydraulic-power generation of electricity which is designed for additional load in electric power system. So hydraulic-power generation of electricity must have fast start reserve. But the amount of water, resources of reservoir-type hydraulic-power generation of electricity is restricted and multi-used, so the scheduling of management by exact forecasting the amount of water is critical. That is why efficient hydraulic-power generation of electricity makes a main role on pumping up the utility of energy and water resource. This thesis introduced the example of optimal generation of electricity plan establishment which is used in managing reservoir-type hydraulic-power generation of electricity.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.127-135
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• 2009

It is necessary to forecast the amount of the maximum electricity demand for stabilizing the flow of electricity. The time series data was collected from the Korea Energy Research between January 2000 and December 2006. The data showed that they had a strong linear trend and seasonal change. Winters seasonal model, ARMA model were used to examine it. Root mean squared prediction error and mean absolute percentage prediction error were a criteria to select the best model. In addition, a nonstationary generalized extreme value distribution with explanatory variables was fitted to forecast the maximum electricity.

• Economic and Environmental Geology
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• v.23 no.4
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• pp.383-392
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• 1990

This study found that that dynamics of intensity of use and economic theory of derived demand can both be accommodated through an extensive translog demand model. The basic idea in this recognition is that the skewed life cycle empirical pattern of intensity of use plotted against per capita income is of lognormal form and this lognomal intensity of use model can be mathematically transformed into an eqivalent simple translog intensity of use model. Empirical results showed that this extensive traslog model, which is a flexible function and includes both the classical case of fixed coefficients and the dynamic case of varying coefficients of the explanatory variables, gave better forecasts than the original intensity of use model and other conventional models.