• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.277-284
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• 2023

It is not easy to implement an energy management system in an industrial complex where small businesses are scattered, so the method of collecting and adjusting energy-related data is mainly used. FEMS is a system that responds to the demand for a paradigm shift from a passive energy management method to an active energy management method using IoT and ICT. In this study, a factory energy management system(FEMS) is designed for small and medium-sized enterprises located in chemical industrial complexes. Efficiency was confirmed by reviewing energy saving measures and efficiencies through FEMS for the electric energy of facilities built in each company. The cost effectiveness of FEMS is created when it is utilized by responsible and empowered personnel within the business processes of the host company. Therefore, it is necessary to utilize EMS that can be applied to the planning, support, operation and evaluation, and continuous improvement of the energy management system to achieve corporate organization and energy management goals.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.453-458
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• 2024

Due to global issues such as climate crisis and rising energy costs, there is an increasing focus on energy conservation and management. In the case of South Korea, approximately 53.5% of the total energy consumption comes from industrial complexes. In order to address this, we aimed to improve issues through the 'Shared Network Utility Plant' among companies using similar energy utilities to find energy-saving points. For effective energy conservation, various techniques are utilized, and stable data supply is crucial for the reliable operation of factories. Many anomaly detection and alert systems for checking the stability of data supply were dependent on Energy Management Systems (EMS), which had limitations. The construction of an EMS involves large-scale systems, making it difficult to implement in small factories with spatial and energy constraints. In this paper, we aim to overcome these challenges by constructing a data collection system and anomaly detection alert system on embedded devices that consume minimal space and power. We explore the possibilities of utilizing anomaly detection alert systems in typical institutions for data collection and study the construction process.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.817-823
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• 2023

Manufacturing plants face the challenge of reducing energy use in response to climate change. Reducing energy consumption can be seen as one of the most important issues, such as reducing production costs and improving efficiency. Among manufacturing industries, the increase in energy consumption in the food industry is gradually increasing along with the improvement of the standard of living and the increase in population. In order to save energy in food processing plants, it is important to identify and analyze energy consumption characteristics in energy-consuming processes. Prior to this, it is necessary to monitor and analyze existing energy consumption to derive reduction measures. In this study, a small and medium-sized food processing plant producing processed meat products was used as a case study to identify and analyze the energy consumption structure at typical cycle/stage level of the batch heating process. From this, we tried to establish realistic and quantitative goals that can be obtained under individual process operating conditions. The results of this study will be used as basic data for the development of diffusion and pervasive energy saving FEMS technology for common core processes of food factories of small and medium-sized enterprises in the future.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 1998.05a
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• pp.117-123
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• 1998

수용가에서 운전중인 전력설비로부터의 실시간 정보는 전기에너지 사용의 효율화는 물론 공장설비의 최적운용 및 향후 설비 신ㆍ증설 계획에 중요한 도움을 줄 수 있다. 이와 관련하여 일부 대형수용가에서는 일찍부터 외국의 감시ㆍ제어 시스템을 도입하여 운용하고 있다. 그러나 국내실정을 충분히 반영할 수 없거나 가격이 고가이므로 중ㆍ소규모 수용가에서는 이에 대한 적용이 사실상 어려운 실정이다. 따라서 국내실정에 적합하고 가격이 저렴한 동시에 독창적이며 우수한 성능을 갖는 퍼지제어형 전력관리시스템을 개발하였다.

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
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• v.44 no.1
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• pp.22-27
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• 2015

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.803-809
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• 2023

This paper analyzes and evaluates to optimally build a data collection system essential for factory energy management systems on an edge-based lightweight platform. A "Sensor/OPC-UA simulator" was developed based on sensors in an actual food factory and used to evaluate the storage engine of edge devices. The performance of storage engines in edge devices was evaluated to suggest the optimal storage engine. The experimental results show that when using the RocksDB storage engine, it has less than half the memory and database size compared to using InnoDB, and has a 3.01 times faster processing time. This study enables the selection of advantageous storage engines for managing time-series data on devices with limited resources and contributes to further research in this field through the sensor/OPC simulator.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.37-39
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• 2022

기존 IoE 환경에서 수집데이터는 특정 서비스를 위한 도메인 지식과 연계되어 서비스를 제공한다. 하지만 수집되는 데이터의 유형이 다양하고, 정적인 지식베이스가 상황에 따라 동적으로 변화하는 IoE 환경에서는 기존의 지식베이스 시스템을 통하여 원활한 서비스를 제공할 수 없었다. 따라서, 본 논문에서는 IoE 환경에서 발생하는 대용량/실시간성 데이터를 시맨틱으로 처리하여 공통 도메인 지식베이스와 연계하고 기존의 지식베이스 추론 방법과 기계학습 기반 지식 임베딩 기법을 통하여 지식 증강을 유기적으로 진행하는 빅시맨틱 시스템을 제시한다. 제시한 시스템은 IoE 환경의 멀티모달(정형, 비정형) 데이터를 수집하고 반자동적으로 시맨틱 변환을 수행하여 도메인 지식베이스에 저장하고, 시맨틱 추론을 통해 지식베이스를 증강 시키며 증강된 지식베이스를 포함한 전체 지식베이스를 정형 및 반정형 사용자 쿼리를 통해 지식정보를 사용자에게 제공한다. 또한, 기계학습 기반 지식 임베딩 기법을 통해 학습·예측을 함으로써, 기존의 지식베이스를 증강하는 기능을 수행한다. 본 논문에서 제시한 시스템은 공장내의 에너지 정보를 수집하여 공정 및 설비 상태 및 운영정보를 바탕으로 실시간 제어를 통한 에너지 절감 시스템인 공장 에너지 관리 시스템의 기반 기술로 구현될 예정이다.

• 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).

• The Safety technology
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• no.199
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• pp.18-20
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• 2014

차세대 친환경 에너지의 대표주자로 꼽히는 태양광 발전. 지구촌의 환경오염 문제가 심각한 문제로 대두되기 시작하면서 세계 각국에서는 태양광 발전 산업에 뛰어들고 있다. 우리나라도 이와 같은 추세에 적극 동침하고 있다. 특히 오늘의 주인공인 한화케미칼(주)은 지난 2010년 태양광 사업을 추진한 이후 지난해 8월부터는 여수에서 폴리실리콘공장을 가동하는 등 본격적인 행보에 나서고 있다. 태양광 발전에서 가장 중요한 요소가 바로 태양전지인데 이의 기초 원료가 되는 폴리실리콘을 이곳에서만 연간 1만톤가량 생산하고 있는 것이다. 이에 힘입어 현재 한화케미칼에서 태양광 사업은 주력 사업으로 부상했을 정도다. 때문에 이곳 공장에서는 그야말로 빈틈없는 안전관리가 전개되고 있다. 작은 사고로도 한화케미칼이 그동안 쌓아온 '안전사업장'이라는 명성에 오점을 남길 수 있기 때문이다. 본사 차원의 시스템을 바탕으로 현장 상황에 맞는 최적의 안전관리를 전개하고 있는 한화케미칼(주) 폴리실리콘공장을 찾아가 봤다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.6
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• pp.229-235
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• 2013

This paper proposed the method to reduce and manage the amount of using power by using the Self-Learning of inference engine that evolves through learning increasingly smart ways for each spaces with in the Space-Based Energy Management System (SEMS, Space-based Energy Management System) that is defined as smallest unit space with constant size and similar characteristics by using the collectible Big Data from the various information networks and the informations of various sensors from the existing Energy Management System(EMS), mostly including such as the Energy Management Systems for the Factory (FEMS, Factory Energy Management System), the Energy Management Systems for Buildings (BEMS, Building Energy Management System), and Energy Management Systems for Residential (HEMS, Home Energy Management System), that is monitoring and controlling the power of systems through various sensors and administrators by measuring the temperature and illumination.