• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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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.

• The Journal of Society for e-Business Studies
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• v.17 no.2
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• pp.105-114
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• 2012

Recently, global warming, energy shortage, and environmental disruption have been serious problems in every nation. It became more and more important to reduce the emission of CO2 and to use of energy efficiently. Smart grid was also introduced using the rapidly developing information technology. It deployed the mutual communication concept between customers and the suppliers in the electricity supply. There were increasing demands to adopt the smart meter and to present incentive for efficient energy usage in many developed countries. The objective of this research was to develop the optimal real time pricing model which maximized the profit of the power retailer and reduced the usage of energy. The simulation study was given to show the usefulness of the model. Simulation considered the customer demand response rate and price elasticity rate. The price elasticity rate was compared in the condition of fixed value according to time and variable value according to the customers. The optimal price model could maximize the profit of the power retailer and reduce the energy usage of the consumers.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2015.05a
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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.

• The Journal of Bigdata
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• v.6 no.1
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• pp.127-132
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• 2021

Korea is both a resource-poor country and a energy-consuming country. In addition, the use and dependence on electricity is very high, and more than 20% of total energy use is consumed in buildings. As research on deep learning and machine learning is active, research is underway to apply various algorithms to energy efficiency fields, and the introduction of building energy management systems (BEMS) for efficient energy management is increasing. In this paper, we constructed a database based on energy usage by device per household directly collected using smart plugs. We also implement algorithms that effectively analyze and predict the data collected using RNN and LSTM models. In the future, this data can be applied to analysis of power consumption patterns beyond prediction of energy consumption. This can help improve energy efficiency and is expected to help manage effective power usage through prediction of future data.

• Journal of the Korean Wood Science and Technology
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• v.42 no.2
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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.

• Land and Housing Review
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• v.3 no.3
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• pp.263-270
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• 2012

Energy consumption of building is given a sizable portion in total national energy conservation. From this reason, the integrated management system of national building energy was proposed to manage energy usage and of which feasibility study was demonstrated in Seoul at 2010. Expansion of the application availability for the aforementioned system, energy policies etc. have focused on the building sectors and future uses and developments are investigated. Specially, energy consumption and building documentary DB are useful to validate energy usage for each building and define to remodelling effect before and after. Furthermore, in this study, a number of developments and applications of the system and future uses of energy usage data can be identified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.8
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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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• 2008.10a
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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.

• Journal of Climate Change Research
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• v.9 no.3
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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.