• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2009.10a
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• pp.7-8
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• 2009

컨테이너터미널에서 사용되는 하역시스템은 유류 및 전기 에너지를 주로 소모하는 특성을 가지고 있어 컨테이너의 작업량에 따라 에너지 소비가 증가하게 된다. 따라서 본 연구에서는 컨테이너터미널 운영사에서 하역작업시 장비별로 소비하는 에너지소비 패턴분석을 하고자 한다. 에너지소비 패턴을 분석하기 위해 하역장비별 에너지 소모량과 영역별 컨테이너 처리량을 상호비교 분석하였다. 그리고 컨테이너터미널에서 소비하는 에너지의 월별 소비패턴에서 정상적인 에너지 소비패턴과 비정상적인 에너지 소비패턴을 분류하는 방법을 도출하고 정상적인 에너지소비 패턴을 유도하기 위한 방안을 제시하고자 한다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2010.10a
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• pp.59-61
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• 2010

컨테이너터미널에서 사용되는 하역장비의 에너지 소비패턴을 분석하여 효율적인 에너지 소비 관리방안을 제시하고자 한다. 컨테이너터미널의 하역장비는 전기에너지와 유류에너지 소비 패턴의 차이가 있으며, 이를 실적자료를 바탕으로 분석하였다. 또한 하역장비의 상호작용에 의한 대기현상을 발생과정을 분석하여 에너지 소비가 많은 애로공정을 도출하였으며, 하역장비 애로공정의 발생지점을 분석하였다. 에너지 소비 관리방안으로 처리량과 에너지 소비 상관계수 비교, 처리물량 변화 패턴 추적 관리, 하역장비 부족에 의한 에너지 소비 애로공정 관리 등의 3가지를 제시하였다.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.09a
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• pp.318-322
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• 2010

본 논문에서는 경기도 에너지 소비량 DB를 구축하고 소비패턴을 분석하였다. 각 에너지원별로 최근 8년간 공급업체의 판매량 자료를 토대로 부문별 사용량을 시군별로 DB를 구축하였다. 경기도 에너지 소비량 분석 결과 2008년 기준 에너지 소비량이 높은 지역은 평택시였으며. 2005년대비 2008년 에너지 증가율이 상대적으로 높은 지역은 화성시, 남양주시, 성남시였다 평택시에는 대규모 국가산업단지가 입지해 있으며, 특히 석유화학과 관련된 업체가 많아 석유류 에너지 소비가 큰 것으로 나타났다. 또한 화성시, 남양주시, 성남시는 택지개발로 인한 급격한 인구증가로 대부분의 에너지가 증가한 것으로 나타났다. 경기도 온실가스를 저감하기 위해서는 이들 시군에 대한 에너지 관리방안이 타 시군보다 우선시 되어야 할 것으로 사료된다. 또한 이를 지속적으로 모니터링 할 수 있는 관리방안도 필요하다. 이러한 에너지 소비를 지속적으로 관리하기 위해서는 모니터링시스템 구축을 통한 관리가 절실하다.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.2
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• pp.47-54
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• 2024

The global decrease in traditional energy resources has prompted increasing energy demand, necessitating efforts to replace and optimize energy sources. This study focuses on enhancing energy efficiency in manufacturing plants, known for their high energy consumption. Through simulations and analyses, the study proposes a temperature-based control system for HVAC (Heating, Ventilating, and Air Conditioning) operations, utilizing machine learning algorithms to predict and optimize factory temperatures. The results indicate that this approach, particularly the prediction-based free cooling algorithm, can achieve over 10% energy savings compared to existing systems. This paper presents that implementing an efficient HVAC control system can significantly reduce overall factory energy consumption, with plans to apply it to real factories in the future.

• Solar Energy
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• v.18 no.3
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• pp.41-50
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• 1998

The purpose of this study is to analyze the energy consumption status for various building types in Taejon. 35 sample buildings were classified into 8 building types, i.e., sports center & swimming pools, hotels, telecommunication exchange service facility, hospitals, research laboratories, department stores, exhibition galleries, universities. According to analyses, energy consumption patterns varies significantly for each building type. Sports centers consumes highest rate(689 $Mcal/sqm{\cdot}yr$) and universities lowest rate(86 $Mcal/sqm{\cdot}yr$) among selected building types.

• 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 Energy Engineering
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• v.22 no.1
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• pp.38-43
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• 2013

There was a dire need to compile data about energy consumption data by use to analyze residential energy consumption patterns relating to changes in lifestyles, or changes in life behavior. Accordingly, bottom-up model for residential energy consumption by residential use was developed by life behavior classification in an attempt to analyze energy consumption. This paper multiplied each appliance's running times by each appliance by life behavior and built a residential bottoms-up model to figure out the energy consumption of each household. The uses by life behavior were broken down into lighting, heating, cooling, entertainment, obtaining information, hygiene, and cooking.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.87-92
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• 2019

This paper describes the pattern analysis for data of the factory energy consumption by using machine learning method. While usual statistical methods or approaches require specific equations to represent the physical characteristics of the plant, machine learning based approach uses historical data and calculate the result effectively. Although rule-based approach calculates energy usage with the physical equations, it is hard to identify the exact equations that represent the factory's characteristics and hidden variables affecting the results. Whereas the machine learning approach is relatively useful to find the relations quickly between the data. The factory has several components directly affecting to the electricity consumption which are machines, light, computers and indoor systems like HVAC (heating, ventilation and air conditioning). The energy loads from those components are generated in real-time and these data can be shown in time-series. The various sensors were installed in the factory to construct the database by collecting the energy usage data from the components. After preliminary statistical analysis for data mining, time-series clustering techniques are applied to extract the energy load pattern. This research can attributes to develop Factory Energy Management System (FEMS).

• Korea Petroleum Association Journal
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• no.5 s.3
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• pp.30-31
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• 1981

• Environmental and Resource Economics Review
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• v.27 no.3
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• pp.463-493
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• 2018

As the population structure of Korea changes with the increase of single households and elderly households, this may have effect on domestic energy consumption pattern. Our study analyzes whether the energy consumption of single and elderly households are distinguishable from those of general households. For empirical analysis, Household Energy Standing Survey panel data and regional fixed effect model are employed. The result strongly shows that single households consume more energy than other households. The consumption of single households from 40s to 60s was the highest. On the other hand, the effect of aging was different from energy sources. Electricity consumption of elderly household was more than other age groups, while oil consumption of elderly household was less than others. Gas and total energy consumptions turned out to be not much different among different age groups.