• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권4호
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• pp.1249-1265
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• 2022

Video streaming has become one of the most popular applications for mobile devices. The network bandwidth required for video streaming continues to exponentially increase as video quality increases and the user base grows. Multi-Path TCP (MPTCP), which allows devices to communicate simultaneously through multiple network interfaces, is one of the solutions for providing robust and reliable streaming of such high-definition video. However, mobile video streaming over MPTCP raises new concerns, e.g., power consumption and cellular data usage, since mobile device resources are constrained, and users prefer to minimize such costs. In this work, we propose a mobile video streaming framework over MPTCP (mDASH) to reduce the costs of energy and cellular data usage while preserving feasible streaming quality. Our evaluation results show that by utilizing knowledge about video behavior, mDASH can reduce energy consumption by up to around 20%, and cellular usage by 15% points, with minimal quality degradation.

• 한국전자거래학회지
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• 제17권2호
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• pp.105-114
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• 2012

최근 지구 온난화와 에너지 고갈 및 환경파괴로 인한 환경 문제에 대하여 세계 각국에서 심각하게 고민하고 있으며, 전기자동차의 상용화를 앞두고 이산화탄소 배출의 저감 및 효율적인 에너지 사용이 중요시 되고 있다. 또한, IT 기술의 발달과 함께 독점적인 전력공급에서 수요자가 참여하는 양방향 커뮤니케이션의 스마트 그리드의 개념이 도입되었다. 주요 국가들에서는 스마트 미터의 보급과 함께 에너지 사용의 효율성 개선을 위하여, 전력 경쟁시장에서 소비자의 수요반응의 활성화를 위한 인센티브의 지급 등의 정책도입이 요구되고 있다. 이에, 본 연구에서는 머지않아 등장할 소매전력시장에서 소비자의 수요반응을 증진하여 소비자의 전기 사용 비용을 절감하며 소매사업자의 이익을 최대화하는 전력 가격결정 모델을 제시하였다. 소비자 수요반응 참여율과 가격 탄성률에 따른 시뮬레이션을 시행하여 수요 반응을 나타내는 소비자 별 탄성률을 모든 소비자가 시간대별 고정 값을 사용하는 것과 소비자 별 탄성률을 예측하는 것을 비교하였다. 이를 통하여 전력 소매 시장에서 소비자의 에너지 사용을 줄이고 소매업자의 이익을 최대화할 수 있음을 밝혔다.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2015년도 춘계 학술논문 발표대회
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• pp.198-199
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• 2015

Construction equipment is a major energy consumption source in construction projects. If 10% reduction of the diesel fuel usage is achieved in the construction industry, it may reduce 5% of the total energy usage. Energy saving operation is a major issue in equipment-intensive operations (e.g., earthmoving or paving operations). Identifying optimal equipment fleet is important measure to achieve low-energy consumption in those operations. This study presents a system which finds an optimal equipment fleet by computing the low-energy performance of earthmoving operations. It establishes construction operation model and compares numerous combinations using alternative equipment allocation plans. It implements sensitivity analysis that facilitates searching the lowest energy consumption equipment fleet by enumerating all cases.

• 한국빅데이터학회지
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• 제6권1호
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• pp.127-132
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• 2021

우리나라는 자원 빈국인 동시에 에너지 다소비 국가이다. 또한 전기 에너지에 대한 사용량 및 의존도가 매우 높고, 총 에너지 사용의 20% 이상은 건물에서 소비된다. 딥러닝과 머신러닝에 대한 연구가 활발해지면서 다양한 알고리즘을 에너지 효율 분야에 적용하려는 연구가 진행되고 있으며, 에너지의 효율적인 관리를 위한 건물에너지관리시스템(BEMS)의 도입이 늘어가는 추세이다. 본 논문에서는 스마트플러그를 이용하여 직접 수집한 가구당 기기별 에너지 사용량을 바탕으로 데이터베이스를 구축하였다. 또한 RNN과 LSTM 모델을 이용하여 수집한 데이터를 효과적으로 분석 및 예측하는 알고리즘을 구현하였다. 추후 이 데이터는 에너지 사용량 예측을 넘어 전력 소비 패턴 분석 등에 적용할 수 있다. 이는 에너지 효율 개선에 도움이 될 수 있으며, 미래 데이터의 예측을 통해 효과적인 전력 사용량 관리에 도움을 줄 것으로 기대된다.

• Journal of the Korean Wood Science and Technology
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• 제42권2호
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• pp.89-100
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• 2014

Energy consumption statistics in 2005 from the Korea Energy Management Corporation show that building energy usage was about 24.2% of total domestic energy consumption, and 64% of total building energy usage was consumed by residential buildings. Thus, about 10% of total domestic energy consumption is due to the heating of residential buildings. Building energy can be calculated by the configuration of the building envelope and the rate of infiltration (the volume of the infiltration of outdoor air and the leakage of indoor air), and by doing so, the annual energy usage for heating and cooling. Therefore, air-tightness is an important factor in building energy conservation. This investigate air infiltration and various factors that decrease it in timber frame buildings and suggest ways to improve air-tightness for several structural types. Timber frame buildings can be classified into light frame, post and beam, and log house. Post and beam includes Han-ok (a Korean traditional building). Six light frame buildings, three post and beam buildings, one Korean traditional Han-ok and a log house were selected as specimens. Blower door tests were performed following ASTM E779-03. The light frame buildings showed the highest air-tightness, followed by post and beam structures, and last, log houses.

• 토지주택연구
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• 제3권3호
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• pp.263-270
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• 2012

정부는 2009년부터 녹색뉴딜사업의 일환으로 국토 에너지 수자원 건물 등의 각종 기본정보를 체계적으로 일원화시키고 통합 DB를 구축하는 녹색국가 정보인프라 사업을 추진 중에 있다. 이와 관련하여 건물부문의 에너지 절약과 온실가스 저감 대책 마련에 효율적으로 대응하기 위한 수단으로, 건물단위의 에너지 소비량 관리를 기본 골자로 하는 건물에너지 통합관리시스템 구축을 진행 중에 있다. 본 시스템의 활용성을 증대시키기 위해서는 기존의 에너지 정책 및 제도와의 관계를 명확히 하고 상호보완적인 요소를 도출하는 것이 요구되며 이는 통합관리시스템의 구축방향성과도 밀접한 관계를 가진다. 따라서 본 연구에서는 장기적 측면에서 국가 건물에너지 통합관리시스템의 효과적인 활용방안을 도모하기 위하여 현재 진행중인 관련 정책 ,제도 및 통계자료 등과의 연계성을 도출하고, 통합관리시스템의 구축방안 및 방향성을 도모하고자 한다. 구체적으로는 온실가스 관련 정책, 녹색 건축물 보급 확대 방안 및 통계자료 등의 사례를 분석하고 통합관리시스템을 통한 해당 정책 및 제도의 개선점을 개선, 보완할 수 있는 방안을 제시함으로서 통합관리시스템의 구축과 운영방안 수립의 당위성을 재고시키며 향후 녹색뉴딜 사업의 육성을 도모하고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.1957-1980
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• 2021

The significant aim of this research has always been to evaluate the mechanism for efficient and inherently aware usage of vitality in-home devices, thus improving the information of smart metering systems with regard to the usage of selected homes and the time of use. Advances in information processing are commonly used to quantify gigantic building activity data steps to boost the activity efficiency of the building energy systems. Here, some smart data mining models are offered to measure, and predict the time series for energy in order to expose different ephemeral principles for using energy. Such considerations illustrate the use of machines in relation to time, such as day hour, time of day, week, month and year relationships within a family unit, which are key components in gathering and separating the effect of consumers behaviors in the use of energy and their pattern of energy prediction. It is necessary to determine the multiple relations through the usage of different appliances from simultaneous information flows. In comparison, specific relations among interval-based instances where multiple appliances use continue for certain duration are difficult to determine. In order to resolve these difficulties, an unsupervised energy time-series data clustering and a frequent pattern mining study as well as a deep learning technique for estimating energy use were presented. A broad test using true data sets that are rich in smart meter data were conducted. The exact results of the appliance designs that were recognized by the proposed model were filled out by Deep Convolutional Neural Networks (CNN) and Recurrent Neural Networks (LSTM and GRU) at each stage, with consolidated accuracy of 94.79%, 97.99%, 99.61%, for 25%, 50%, and 75%, respectively.

• 전기학회논문지
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• 제63권8호
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• pp.1111-1115
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• 2014

With the increasing interest of a demand side management using a Smart Grid infrastructure, the demand resources and energy usage data management becomes an important factor in energy industry. In addition, with the help of Advanced Measuring Infrastructure(AMI), energy usage data becomes a Big Data System. Therefore, it becomes difficult to store and manage the demand resources big data using a traditional relational database management system. Furthermore, not many researches have been done to analyze the big energy data collected using AMI. In this paper, we are proposing a Hadoop based Big Data system to manage the demand resources energy data and we will also show how the demand side management systems can be used to improve energy efficiency.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2008년도 추계학술대회 논문집
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• pp.372-374
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• 2008

Korea classified into a development country when UNFCCC was concluded in 1995. So Korea doesn't have a GHG reduction duty until 2012. As the UNFCCC is strengthened, recently there is a growing interest in renewable energy and energy usage efficiency improvement for reducing GHG emission. It is associated with CES and renewable energy. CES is a total energy (heat, cooling and power)supplier in aggregated demand zone like a hotel, building, hospital and redevelopment district using CHP and it improves energy usage efficiency. At present, renewable energy is needed for GHG reduction duty but renewable energy doesn't have economic feasibility. So renewable energy is needed various support system to popularize which is a FIT and RPS. Especially RPS is carrying out instead of FIT in many advanced country and it will be inroduced in Korea. RPS is a duty which electricity service provider must guarantee renewable energy as much as specific ratio of total capacity. Therefore this study conducts an economic feasibility estimation of CES considering renewable energy when RPS will introduced in the future.

• 한국기후변화학회지
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• 제9권3호
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• pp.253-261
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• 2018

As user-involved energy saving activities have become important in recent years, many forms of energy feedback experiments have been conducted. We conducted a study to determine if energy feedback activities affect energy saving for students living in dormitories at a university in Seoul. In particular, smart plugs were used for efficient research and quantitative performance measurements, and the extent of the impact of competition and rewards on participant energy saving behavior was further analyzed. The main findings of this study are as follows. First, the power usage of groups using smart plugs was lower than that of those without them. Second, energy feedback delivered to smart plug users did not have a significant impact on reduction of electric power consumption. Third, competition and compensation strategies had additional effects in reducing power usage for smart plug users. As a result, methods to deliver energy feedback more effectively as ICT technologies develop and efficient energy activities using IoT technologies can be expected to spread widely in the future.