• JOURNAL OF ELECTRICAL WORLD
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• s.315
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• pp.7-9
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• 2003

2002년도 전력소비량은 2,785억kW로서 작년보다 $8.0\%$ 증가하였으며, 12월중 소비량은 248억kW($8.2\%$증가)로 월간 최대를 기록하였다. 이처럼 전력수요가 크게 증가한 것은 8, 9월중 많은 강우와 저기온 영향으로 냉방수요 증가가 둔화되었음에도 불구하고, 10월부터 일찍 시작된 한파의 영향으로 난방전력 수요가 크게 증가한 때문으로 판단된다. $\divideontimes$겨울철 난방전력 민감도 조사에 의하면 총난방전력수요는 665만kW이며, 겨울철 기온이 $1^{\circ}C$하락함에 따라 31만kW의 난방전력이 증가하는 것으로 나타났다. 2002년도 용도별 소비량은 주택용이 $11.8\%$의 높은 증가율을 보였는데 이는 심야전력($24.9\%$ 증가)을 비롯한 난방전력의 수요가 증가한 때문이다. 또한 일반용($9.1\%$ 증가) 및 교육용($11.9\%$ 증가)도 건실한 증가율을 나타냈다. 한편, 산업용전력은 반도체, 기계장비 및 자동차업종 등 수출주도형 업종의 소비량은 높았으나, 소비 비중이 높은 화학제품 및 섬유업종의 전력소비 둔화로 평균증가율보다 낮은 $6.4\%$ 증가에 그쳤다. 그러나 전체 전력소비량에서 차지하는 비중은 $54.3\%$(1,512억kW)로 여전히 제일 높았다. 지역별로는 수도권이 1,049억kWh를 소비하여 전체의 $37.7\%$ 부산$\cdot$경남은 532억kWh로서 $19.1\%$를 점유하였다.

• Journal of Navigation and Port Research
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• v.33 no.3
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• pp.241-245
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• 2009

Because of the heat island phenomenon and sea wind, there can be thermal conditions' differences around buildings at downtown and coastal area respectively in coastal city, like Busan, Incheon, Mokpo. For the final purpose of the buildings' energy saving design and operation considering of above mentioned environments differences, energy consumption including heating and cooling loads, electric loads are necessary to be accumulated and analyzed in as the database. As a part of this concept, this study aims to survey and analyze each loads of 22 apartments which has at least 100 households respectively and is located at Yeongdo island, Busan, Korea It is cleared that despite the residents living in this district can use sea wind as a natural ventilation and/or cooling methods, they mainly depends on the electric-driven air-conditioners for cooling with window-closed because of anti-salt problems of the sea wind. This leads the maximum power consumption of the surveyed-22-apartments to be appeared in August like that of inland buildings.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.421-424
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• 2013

최근 문제가 되고 있는 전력 문제를 효율적으로 관리하기 위해 건물 에너지 관리 시스템이 주목받고 있다. 건물 에너지 관리 시스템은 관리자가 건물의 전력 소비량을 효율적으로 관리할 수 있도록 전력 소비량에 대한 모니터링 기능을 제공하는 시스템이다. 기존의 건물 에너지 관리 시스템은 과거, 현재, 미래의 전력 소비량을 통계 자료로 제공하고, 이를 토대로 전력 과부하 발생을 방지하였다. 그렇지만 기존의 시스템에 반응형 웹 디자인을 적용한 사례를 찾아보기 힘들며 온도 변화에 따른 전력 소비량을 고려하지 않기 때문에 정확한 부하 예측을 하기 어렵다는 단점이 있다. 본 논문에서 제안한 건물 에너지 관리 시스템은 반응형 웹 디자인을 적용하여 여러 모바일 기기로도 편리하고 효율적으로 건물을 관리할 수 있게 하였다. 또한, 건물에서 유지되어야 할 목표 온도, 건물 전력 소비량에 대한 과거 데이터와 기상청에서 제공하는 데이터를 통하여 부하 예측을 하고, 다양한 전력 소비량 통계 자료를 제공한다. 이를 통해 관리자는 효율적인 건물 에너지 관리를 할 수 있다.

• Journal of Energy Engineering
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• v.29 no.1
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• pp.1-12
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• 2020

The rapid climate change is strengthening carbon emissions regulations internationally. Korea is strongly pressed to accept the obligation to reduce greenhouse gases as one of the United Nations Framework Convention on Climate Change. This article analyzed the Granger causalities among environmental regulation, economic growth, electricity consumption, and CO₂ emission in Korea, using unit root test, cointegration test, and vector error correction model. As the results, environmental regulation has shown the bidirectional causalities with electricity consumption and CO₂ emission, while being unilaterally affected by economic growth in the long-run and strong relationship. Economic growth has affected electricity consumption, CO2 emission, and environmental regulation in the long-run, in the complex structure of the unilateral and short-run causality with electricity consumption and the bidirectional causality with CO2 emission. The policy implications will be as follows: ① environmental regulation should induce sustainable growth through encouraging technological innovation relating to CO2 reduction and productivity enhancement. ② Responding to the international CO2 reduction regulation, the synthetic policy initiatives will be considered to make synergy effects among policies relating to economic growth, electricity consumption.

• Journal of the Korea Convergence Society
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• v.8 no.1
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• pp.19-23
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• 2017

Energy is a vital resource for the economic growth and the social development for any country. As the industry becomes more sophisticated and the economy more grows, the electricity demand is increasing. So forecasting electricity demand is an important for electricity suppliers. Forecasting electricity demand makes it possible to distribute electricity demand. As the market for Negawatt market began to grow in Korea from 2014, the prediction of electricity consumption demand becomes more important. Moreover, power consumption forecasting provides a way for demand management to be directly or indirectly participated by consumers in the electricity market. We use Genetic Algorithms to predict the energy demand of the fishing industry in Jeju Island by using GDP, per capita gross national income, value add, and domestic electricity consumption from 1999 to 2011. Genetic Algorithm is useful for finding optimal solutions in various fields. In this paper, genetic algorithm finds optimal parameters. The objective is to find the optimal value of the coefficients used to predict the electricity demand and to minimize the error rate between the predicted value and the actual power consumption values.

• Journal of Navigation and Port Research
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• v.36 no.6
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• pp.501-506
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• 2012

To serve basic data for the design of capacity and management of Distributed(or On-site) Power Generation System using renewable energies, this study measured the electric power consumption(hereafter abbreviated as EPC) of 5 families of fishing village located at island in southeastern area of Korea. The results are as following. The maximum monthly average EPC occurred in December or January. Although the total monthly EPC of H family is 2~3 times more than J family, individual monthly EPC of J family is 10~30 % more than H family. Hourly EPC pattern shows that the maximum EPC occurred between 20~24 o'clock in summer season, but it occurred between 18~24 o'clock in winter season. Compared to summer, the height of fluctuation through a day is small. And the EPC patterns of weekdays and weekend estimated as very similar.

• Journal of the Society of Disaster Information
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• v.18 no.3
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• pp.532-541
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• 2022

Purpose: The purpose of this study is to suggest that the existing power demand prediction method including power demand according to fine dust is included in the existing power consumption by using an air purifier to improve the air quality due to fine dust. Method: The method of the study was compared and analyzed using data on the concentration of fine dust in Seoul for three years, household power consumption, and climate observation, and the effect of fine dust on power consumption in Seoul was identified in April and October. Result: The power consumption of home air purifiers in Seoul due to fine dust differences between April and October was calculated to be 2,141 MWh, accounting for 3.4% of the total difference in the use of home appliances in April and October. Conclusion: The effect of fine dust on household power consumption was verified, and power demand prediction is essential for economic system operation and stable power supply, so power consumption due to fine dust should be considered as well as focusing on power consumption of existing air conditioners and heaters.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2002.11a
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• pp.297-302
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• 2002

지속적인 경제성장으로 초고층 복합 첨단정보빌딩이 급속히 증가하고, 국민생활 수준의 질적향상으로 쾌적한 환경 요구에 부응하여 고급에너지인 전력 소비량이 매년 10%이상 높게 증가하고 있어 효율적인 전력관리가 절실히 요구되고 있다. 전력소비량이 많은 산업(제조업)분야와 건축물분야 다소비시설물의 전력원단위를 분석하고 효율적인 전력관리 방안을 제시하여 전력사용합리화를 이룩하고자 한다.

• Journal of IKEEE
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• v.22 no.3
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• pp.822-828
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• 2018

Recently, energy issues such as massive blackout due to increase in power consumption have been emerged, and it is necessary to improve the accuracy of prediction of power consumption as a solution for these problems. In this study, we investigate the difference between the actual power consumption and the predicted power consumption through the deep learning- based power consumption forecasting experiment, and the possibility of adjusting the power reserve ratio. In this paper, the prediction of the power consumption based on the deep learning can be used as a basis to reduce the power reserve ratio so as not to excessively produce extra power. The deep learning method used in this paper uses a learning model of long-short-term-memory (LSTM) structure that processes time series data. In the computer simulation, the generated power consumption data was learned, and the power consumption was predicted based on the learned model. We calculate the error between the actual and predicted power consumption amount, resulting in an error rate of 21.37%. Considering the recent power reserve ratio of 45.9%, it is possible to reduce the reserve ratio by 20% when applying the power consumption prediction algorithm proposed in this study.

• 전기의세계
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• v.20 no.4
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• pp.49-58
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• 1971

블란서 본토의 에너지자원은 북부에서 생산되는 석탄, 삐레네 산록지대의 천연가스와 알프스산맥, 삐레네산맥 및 중앙산지에 분포되어 있는 수력자원이 있으나 국내에너지소비량을 충족하기에 부족한 형편이므로 인접국가로부터 석탄, 석유 및 전기의 수입에 의존해오고 있으며 블란서의 전력은 수력, 화력 및 원자력을 합해서 나라전체 에너지소비량의 약 11%를 감당하고 있는데 아래에 EDF(블란서 전력공사)의 1969년도 보고서(Report)를 중심으로 한 블란서의 전기사정을 살펴보기로 한다.