• Journal of Environmental Impact Assessment
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• v.22 no.6
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• pp.667-676
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• 2013

This study analyzed the energy demand, greenhouse gas emission and greenhouse gas reduction potential of Electronic Electrical components company. The LEAP model targeting long term energy plan was used to establish the most efficient plan for the companies by examining the climate change policy of government and the countermeasures by companies. A scenario was created by having 11 greenhouse gases reduction plans to be introduced from 2011 as the basic plan. Regarding input data, energy consumption by business place and by use, number of employee from 2009 to 2012, land area and change in number of business places were utilized. The study result suggested that approximately 13,800 TJ of energy will be spent in 2020, which is more than 2 times of 2012 energy consumption. When the integrated scenario based on the reduction plan of companies would be enforced, approximately 3,000 TJ will be reduced in 2020. The emission of greenhouse gases until 2020 was forecasted as approximately 760,000 ton $CO_2eq$. When the integrated scenario would be enforced, the emission will be approximately 610,000 ton $CO_2eq$, which is decrease by approximately 150,000 ton $CO_2eq$. This study will help the efficient responding of eElectronic Electrical components company in preparing detail report on objective management system and enforcement plan. It will also contribute in their image as environment-friendly companies by properly responding to the regulation reinforcement of government and greenhouse gases emission target based on environment policy.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.861-864
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• 2011

Since national GHG reduction target by 2020 has been presented in Korea, the role of railroad has been reinforced within transport system due to the allocation of reduction target into sector. So, it is necessary to manage activity data systematically for the calculation of GHG emission in railroad. Now, the activity data of diesel consumption for NIR(National Inventory Report) are provided from oil supply and demand statistics. On the other hands, the activity data collected directly from railroad operating companies are used for GHG & Energy Target Management Act. This study aimed to assess the GHG emissions using two kinds of activity data related to the diesel consumption of railroad in 2009 and 2010. As a result, GHG emissions based on oil supply and demand statistics was 636 thousands ton $CO_{2e}$, but the activity data collected from railroad operating companies showed 649 thousands ton $CO_{2e}$ in 2009. Also, the gap of $CO_{2e}$ emission was increased in 2010. These trends were caused because oil supply and demand statistics included total diesel sales volume during 1 year and the activity data collected from railroad operating companies were the amount of diesel consumption only at railcar operation and maintenance step. In conclusion, it is important to develop the management and verification system of activity data with high reliability to substitute oil supply and demand statistics in railroad sector.

• Clean Technology
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• v.17 no.2
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• pp.156-165
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• 2011

The process for bioethanol production from lignocellulosic biomass was studied through process simulation using PRO/II. Process integration was conducted with concentrated acid pretreatment, hydrolysis process, SMB (simulated moving bed chromatography) process and pervaporation process. Energy consumption could be minimized by the heat recovery process. In addition, material and energy balance were calculated based on the results from the simulation and literature data. A net production yield of 4.07 kg-biomass and energy consumption value of 3,572 kcal per 1 kg ethanol were calculated, which is indicating that 26% yield increase and 30% energy saving compared to the bioethanol production process with dilute-acid hydrolysis (SRI report). In order to make it possible, sugar conversion yield of cellulose and hemi-cellulose is to be reached up to 90% and fermentation of xylose needs to be developed. In order to reduce the energy consumption up to 30%, the concentration of acid solution after being separated by 5MB should exceed 20%. If acid/sugar separation by SMB process is to be practical, the bioethanol process designed in this study can be commercially feasible.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.21-29
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• 2011

Wireless sensor nodes operate in open environments. The deployed sensor nodes are very vulnerable to physical attacks from outside. Attackers compromise some sensor nodes. The compromised nodes by attackers can lead to false data injection into sensor networks. These attacks deplete the limited energy of sensor nodes. Ye et al. proposed the Statistical En-Route Filtering (SEF) as a countermeasure of the attacks. The sensor node in SEF examines the event reports based on certain uniform probability. Thus, the same energies are consumed in both legitimate reports and false reports. In this paper, we propose a method that each node controls the probability of attempts to verify a report to reduce energy consumption of sensor nodes. The probability is determined in consideration of the remaining energy of the node, the number of hops from the node to SINK node, the ratio of false reports. the proposed method can have security which is similar with SEF and consumes lower energy than SEF.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.31-40
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• 2011

Sensor nodes are easily exposed to malicious attackers by physical attacks. The attacker can generate various attacks using compromised nodes in a sensor network. The false report generating application layers injects the network by the compromised node. If a base station has the injected false report, a false alarm also occurs and unnecessary energy of the node is used. In order to defend the attack, a statistical en-route filtering method is proposed to filter the false report that goes to the base station as soon as possible. A path renewal method, which improves the method, is proposed to maintain a detection ability of the statistical en-route filtering method and to consume balanced energy of the node. In this paper, we proposed the secure path cycle method to consume effective energy for a path renewal. To select the secure path cycle, the base station determines through hop counts and the quantity of report transmission by an evaluation function. In addition, three methods, which are statistical en-route filter, path selection method, and path renewal method, are evaluated with our proposed method for efficient energy use. Therefore, the proposed method keeps the secure path and makes the efficiency of energy consumption high.

• Journal of the Korean Solar Energy Society
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• v.29 no.4
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• pp.41-47
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• 2009

Wind/diesel hybrid operation can be one of the most effective option for electrical power production at a remote area such as Antarctica. The king Sejong station at Antarctica relies its power production on diesel engines and diesel oil is supplied every other year by ships. However, the oil transportation processes are liable to potential oil spillage caused by the floating ice around the King George island. The long-term storage of the oil at the station can also contaminate the surrounding soils. A l0kW wind turbine has been installed to save oil consumption and operated in connection with the diesel generators since 2006. The diesel engine that operated poorly during the first year of installation was replaced in 2008 to enhance power production an recent measurements indicate that both diesel power quality and the wind turbine availability have been dramatically improved by the replacement. This report discusses electrical power qualities of wind/diesel hybrid system operating at an isolated micro gird located in the king Sejong station. Our experience reveals that the similar technologies can be applied to domestic islands, for example, in the south sea.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06a
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• pp.261-262
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• 2008

Due to technological advances, the manufacturing of small and low cost of sensors becomes technically and economically feasible. In recent years, an increasing interest in using Wireless Sensor Network (WSN) in various applications, including large scale environment monitoring, battle field surveillance, security management and location tracking. In these applications, hundreds of sensor nodes are left to be unattended to report monitored data to users. Since sensor nodes are placed randomly and sometimes are deployed in underwater. It is impossible to replace batteries often when batteries run out. Therefore, reducing energy consumption is the most important design consideration for sensor networks.

• Journal of KIBIM
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• v.4 no.2
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• pp.1-9
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• 2014

As interest in green building has increased, construction market has evolved through BIM-based architecture also, BIM-based technologies have been developed simultaneously. Due to this aspect, the need of environmental analysis software utilizing BIM data became essential. This study shows that BIM-based integrated module provides objective analysis to proceed quick decision-making for a proposal. In addition to that, this integrated module creates a model through BIM data to analyze and report residential environment and energy consumption such as, daylight, view, ventilation and privacy in order to practically apply the BIM technology from the schematic design.

• 한국태양에너지학회:학술대회논문집
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• 2011.04a
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• pp.54-58
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• 2011

According to the Fourth Assessment Report of the Inter governmental Panel on Climate Change(IPCC), climate change is already in progress around the world, and it is necessary to start mitigation and adaptation strategies for buildings in order to minimize adverse impacts. It is likely that the South Korea will experience milder winters and hotter and more extreme summers. Those changes will impact on building performance, particularly with regard to cooling and ventilation, with implications for the quality of the indoor environment, energy consumption and carbon emissions. This study generate weather data for future climate change for use in impacts studies using PRECIS (Providing REgional Climate for Impacts Studies). These scenarios and RCM (Regional Climate Model) are provided high-resolution climate-change predictions for a region generally consistent with the continental-scale climate changes predicted in the GCM (Global Climate Model).

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.162-175
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• 2018

Currently, electricity consumption and feedback mechanisms are being widely researched in Internet of Things (IoT) areas to realise power consumption monitoring and management through the remote control of appliances. This paper aims to develop a smart electricity utilisation IoT platform with a deep belief network for electricity utilisation feature modelling. In the end node of electricity utilisation, a smart monitoring and control module is developed for automatically operating air conditioners with a gateway, which connects and controls the appliances through an embedded ZigBee solution. To collect electricity consumption data, a programmable smart IoT gateway is developed to connect an IoT cloud server of smart electricity utilisation via the Internet and report the operational parameters and working states. The cloud platform manages the behaviour planning functions of the energy-saving strategies based on the power consumption features analysed by a deep belief network algorithm, which enables the automatic classification of the electricity utilisation situation. Besides increasing the user's comfort and improving the user's experience, the established feature models provide reliable information and effective control suggestions for power reduction by refining the air conditioner operation habits of each house. In addition, several data visualisation technologies are utilised to present the power consumption datasets intuitively.