• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.8
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• pp.1575-1580
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• 2017

The electric vehicle business is important because it can reduce 30% of the fine dust generated in the metropolitan area and it can solve the air pollution problem by replacing automobile exhaust gas from an internal combustion engine with eco-friendly electric cars. For the construction of the electric charging station infrastructure, which is the core part of the electric car business, we focus to select the optimal location of the electric car charging station in Seoul. The goal of this paper is to utilize and analyze the traffic statistics of T-Map navigation users data and Seoul Metropolitan Transportation Policy Department to deploy the electric cars charging station with optimal location to increase the efficiency. In this paper, the proposed algorithm is composed of two parts of electric charging station selection. First, we analyze real traffic statistics and area. Second, we utilize T-Map navigation data distribution. To select optimal electric charging station location, we apply these two algorithms.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.47-56
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• 2023

Hydrogen energy is an eco-friendly energy that produces heat and electricity with high energy efficiency and does not emit harmful substances such as greenhouse gases and fine dust. In particular, smart hydrogen energy is an economical, sustainable, and safe future smart hydrogen energy service, which means a service that stably operates based on 'data' by digitally integrating hydrogen energy infrastructure. In this paper, in order to implement a data-based hydrogen charging station demand forecasting model, three hydrogen charging stations (Chuncheon, Sokcho, Pyeongchang) installed in Gangwon-do were selected, supply and demand data of hydrogen charging stations were secured, and 7 machine learning and deep learning algorithms were used. was selected to learn a model with a total of 27 types of input data (weather data + demand for hydrogen charging stations), and the model was evaluated with root mean square error (RMSE). Through this, this paper proposes a machine learning-based hydrogen charging station energy demand prediction model for optimal hydrogen energy supply and demand.

• LP가스
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• s.72
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• pp.44-59
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• 2001

이 자료는 우리 협회가 에너지경제연구원에 용역의뢰한 "LPG가격자유화시 충전소 대응방안 및 정적마진 산정에 관한 연구"라는 제목의 최종보고서를 요약한 것이다.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.577-585
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• 2023

This study applied ALOHA Program to predict the damage caused by fire and explosion predicted to occur from gas leakage at LPG stations and presented plans to prevent damages by diagramming the impact range and distance. The propane gas leakage from LPG stations causes human damage like breathing issues and property damage, including building destruction to residents in the surrounding areas. As a way to reduce this, first, the hazardous substance safety manager of the LPG station needs to check frequently whether the meters and safety valves are working properly to prevent leakage in advance. Second, the LPG stations' storage tanks should be worked by the person who received "hazardous substance safety manager training" under the provisions of the Act on the Safety Control of Hazardous Substances and has been appointed as a "hazardous substance safety manager" by the fire department. Third, LPG station's various safety device functions, such as overfill prevention devices, must be checked on a regular basis. Finally, wearing work clothes and shoes that prevent static electricity at LPG stations is highly recommended, as static can cause a fire when gas leaks.

• LP가스
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• v.23 no.4
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• pp.24-25
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• 2011

현행 LPG충전소는 액화석유가스 충전시설의 시설기준 및 기술기준(액법 시행규칙 별표 3, 16항 및 KGS CODE 331~334)에 따라 비상발전기를 무조건 보유해오고 있었으나, 우리협회는 지식경제부 및 한국가스안전공사 등에 지속적 건의를 통해 관련규정 "비상전력설비 설치기준(KGS CODE)"을 개정(2011.07.27 시행)하여 비상발전기를 선택적으로 보유할 수 있도록 하였으니 충전소 운영에 참고하시기 바랍니다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.370-372
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• 2017

Recently, at home and abroad, as the demand and supply of electric vehicles have increased, the spread of charging stations for electric vehicles is accordingly spreading out. In this paper, we provide the location, price, and service information of the electric car charging station through the big data analysis by using Open API of the public data and evaluation service provided by KEPCO, thereby inducing communication among users.

• LP가스
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• s.72
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• pp.39-43
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• 2001

자동차충전소의 증가율이 예사롭지 않다. 기껏해야 한 해에 20-30개소씩 증가하던 것이 한 해 동안 무려 다섯 배에 달하는 신규허가가 등장하는가 하면 허가대기중인 업소 역시 수백개소를 헤아리고 있다. 여기에 최근 계속되고 있는 그린벨트내 충전소 배치계획을 보면 지나치게 많은 충전소가 양산될 조짐을 보이는 등 자칫 제2의 주유소 파동까지 우려되고 있다. 충전소의 난립 조짐에 반해 LPG자동차의 증가는 정반대의 양상으로 치닫고 있다. 99년 2000년 두해 동안 LPG차가 비정상적으로 증가하자 정부가 이를 억제하기 위해 대폭적인 특소세 인상이라는 대형 악재를 터트렸기 때문이다. 특소세 인상과 하게 하늘 높은줄 모르고 치솟고 있는 LPG가격도 자동차 연료로서의 가격경쟁력을 약화시켜 이 역시 LPG자동차 보급확대의 걸림돌로 작용하고 있다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.241-242
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• 2022

지속 가능한 발전을 위해 전기자동차의 관심 및 수요가 늘어나는 상황에서 전기자동차 충전소 및 주차공간에 대한 정보 습득은 더욱 중요해졌다. 서비스 이용자는 본인의 위치를 기반으로 하여 가까운 주차장과 전기 충전소의 유무를 확인할 수 있는 DBMS를 설계하고 구축하였다. 이용자에게 주차장 및 전기 충전소에 대한 정보를 얻는 과정에서 더욱 편리함을 안겨주는 것을 목표로 하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.131-138
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• 2023

Globally, the eco-friendly industry is developing due to the climate crisis. Electric vehicles are an eco-friendly industry that is attracting attention as it is expected to reduce carbon emissions by 30~70% or more compared to internal combustion engine vehicles. As electric vehicles become more popular, charging stations have become an important factor for purchasing electric vehicles. Recent research is using artificial intelligence to identify local demand for charging stations and select locations that can maximize economic impact. In this study, in order to contribute to the improvement of the performance of the electric vehicle charging station demand prediction model, nationwide data that can be used in the artificial intelligence model was defined and a pre-processing technique was proposed. In addition, a preprocessor, artificial intelligence model, and service web were implemented for real charging station demand prediction, and the value of data as a location selection factor was verified.

• Smart Media Journal
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• v.9 no.1
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• pp.67-74
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• 2020

As the demand of electric vehicles has been increased recently, their related industries are developing. In particular, the market of electric vehicles charging infrastructure is expanding rapidly and users have much demand in convenience of electric vehicles charging site. Because an charging site using electric pole of KEPCO can make use of that installed nearby public parking lots, there are many commercial construction and convenient facilities near around it. In this situation, users can do shopping or their personal business during charging. In this paper, we proposed the design of charging platform for electric vehicles to support LBS for users to do shopping or personal business conveniently.