• Nuclear industry
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• v.37 no.7
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• pp.9-12
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• 2017

전국 60개 대학교 공과대학 교수 중심의 전임교수 417명이 참여한 '책임성 있는 에너지 정책수립을 촉구하는 교수 일동'은 7월 5일 오전 10시30분 국회 정론관에서 기자회견을 열고, 합리적인 에너지 공론화가 필요하며 국회를 비롯한 국가의 정상적 의사 결정 체계가 작동해야 한다고 강력 촉구했다. 참여 대학은 공과대학 소속 79명의 전임교수가 참여한 서울대(82명)를 비롯해 부산대(58명), KAIST(43명), 조선대(32명), UNIST(15명), 경희대(16명), 한양대(17명), 중앙대(13명) 등 60곳이며, 미국 퍼듀(Purdue)대, 미시간대(U. Michigan) 등 외국 대학 4곳도 포함됐다. '책임성 있는 에너지 정책수립을 촉구하는 교수 일동'명의의 성명서 발표는 지난 6월 1일 성명서에 이어 두 번째다. 성명서 전문과 설명 자료('정부 탈원전 기조의 문제점')를 게재한다.

• The Journal of Sustainable Design and Educational Environment Research
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• v.17 no.3
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• pp.1-8
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• 2018

We suggest improvement direction of energy-saving design based on analysis of current situation in school facilities. School facilities have large area among public buildings. While the number of students is decreasing, the number of school and energy consumption is increasing year after year. School facilities have excellent heat insulation property, but it requires further examination about excessive heat insulation plan. School facilities are using gas heat pump actively for cooling and heating, but has difference in use ratio of ground source heat pump by region. Thus School facilities requires active using of ground source heat pump and BIPV(Building Integrated Photovoltaic System).

• The Journal of Bigdata
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• v.5 no.1
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• pp.227-239
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• 2020

This paper introduces a method for predicting the maximum power demand despite communication errors in industrial sites. Due to the recent policy of de-nuclearization in Korea, the price of electricity is inevitable, and the amount of electricity used and maximum load management for the management of power demand are becoming important issues. Accordingly, it is important to predict and manage peak power. However, problems such as loss and modulation of measured power data occur at industrial sites due to noise generated by various facilities and sensors. It is difficult to predict the exact value when measured effective power data are lost. The study presents a model for predicting and correcting anomalies and missing values when measured effective power data are lost. The models used in this study are expected to be useful in predicting peak power demand in the event of communication errors at industrial sites.