• 한국재난정보학회 논문집
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• 제18권3호
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• pp.532-541
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• 2022

연구목적: 본 연구의 목적은 대기의 미세먼지로 인해 공기의 질을 개선하고자 공기청정기를 사용함으로써, 발생하는 가정용 전력소비량을 확인해 기존의 전력 수요예측 방식에 미세먼지에 따른 전력수요를 포함하여 예측할 것을 제안하고자 한다. 연구방법: 연구의 방법은 서울지역의 3년간 미세먼지의 농도, 가정용 전력소비량, 기후관측 데이터를 활용하여 비교분석하였고, 4월과 10월을 대상으로 미세먼지가 서울지역 전력소비량에 미치는 영향을 파악하였다. 연구결과: 연구의 결과는 4월과 10월의 미세먼지 차이에 의한 서울지역 가정용 공기청정기 전력소비량이 2,141MWh로 산정되어, 4월과 10월의 가전기기 사용의 전체 차이의 3.4%에 해당한다. 결론: 미세먼지가 가정용 전력소비량에 미치는 영향을 확인하였으며, 전력 수요예측은 경제적인 계통 운영과 안정적인 전력 수급에 필수적이므로, 기존의 냉·난방기의 전력 소비에 중점을 두어 예측하는 것뿐만 아니라, 미세먼지로 인한 전력 소비도 고려해야 한다.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제4권2호
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• pp.47-56
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• 2015

에너지 절감형 서버 클러스터 환경에서는 서버 전원 모드가 부하상황에 따라 제어된다. 다시 말하면 현재 부하를 처리하는 데 필요한 대수의 서버들만 ON하고 나머지 서버들은 OFF한다. 이 알고리즘은 정상적인 상황에서는 잘 동작하지만 부하가 급증 또는 급감하는 비정상적인 상황에서는 QoS를 보장할 수 없다. 왜냐하면 서버가 OFF에서 ON으로 바뀌는 데 필요한 지연시간 때문에 ON 서버 대수를 당장 증가시킬 수 없기 때문이다. 본 논문에서는 정상적인 상황뿐만 아니라 비정상적인 상황에서도 QoS를 향상시키는 새로운 소비 전력 예측 알고리즘을 제안한다. 제안된 예측 알고리즘은 기존 시계열 분석에 기반한 예측과 추세를 반영한 예측 조정의 두 부분으로 구성된다. 15대의 서버 클러스터를 이용하여 실험이 수행되었고, 4가지 유형의 기존의 시계열 예측 모델과 본 논문에서 제안하는 4가지 유형의 수정된 모델에 대해 성능을 비교하였다. 실험 결과 4가지 유형 중 추세조정 지수평활법(ESTA)과 본 논문에서 제안된 ESTA(MESTA)가 표준화된 QoS 및 단위전력당 좋은 응답수 측면에서 가장 우수한 성능을 보였으며, 또한 본 논문에서 제안한 MESTA 알고리즘이 기존의 ESTA 알고리즘에 비해 가상 부하패턴과 실제 부하패턴에 대해 QoS가 7.5%, 3.3% 각각 향상됨을 보여주었다.

• 한국멀티미디어학회논문지
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• 제24권1호
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• pp.67-74
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• 2021

As IoT(Internet Of Things) devices like a smart sensor have constrained power sources, a power strategy is critical in WSN(Wireless Sensor Networks). Therefore, it is necessary to figure out the residual power of each sensor node for managing power strategies in WSN, which, however, requires additional data transmission, leading to more power consumption. In this paper, a residual power estimation method was proposed, which uses ignorantly small amount of power consumption in the resource-constrained wireless networks including WSN. A residual power prediction is possible with the least data transmission by using Machine Learning method with some training data in this proposal. The performance of the proposed scheme was evaluated by machine learning method, simulation, and analysis.

• 한국정밀공학회지
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• 제32권4호
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• pp.329-337
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• 2015

In this paper, a simulation based estimation method of energy consumption of the spindle and feed drives for the NC machine tool during the cutting process is proposed. To predict energy consumption of the feed drive system, position, velocity, acceleration and jerk of the table are analyzed based on NC data and then the power and energy are calculated considering friction force and mass of the stages. Energy consumption of the spindle is estimated based on models from acceleration motion of rotating parts, friction torque and power loss of motors. Moreover, simulation models of cutting power and energy for the material removal along the NC tool paths are proposed.

• 한국인터넷방송통신학회논문지
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• 제19권5호
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• pp.93-99
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• 2019

본 논문에서는 효율적 에너지 관리를 위해 인공 신경망 중 하나인 GRU 모델을 사용하여 전력사용량을 예측하고 예측된 전력사용량과 실제 전력사용량의 비교를 통해 부하를 자동 제어 하는 스마트 미터기를 제안한다. 제안한 스마트 미터기를 통해 GRU 모델을 학습시키기 위해 필요한 전력사용량 데이터를 수집했다. 구현된 스마트 미터기가 전력사용량 자동측정 및 실시간 관찰 기능과 전력사용량 예측을 통한 부하 제어 기능을 가지고 있음을 보여준다. 성능평가 지표 중 하나인 Root Mean Squared Error (RMSE) 값에 약 20%의 마진 값을 이용하여 부하 자동 제어를 위한 기준 값으로 설정했다. 부하 자동 제어 기능을 가진 스마트 미터기로 인해 에너지 관리의 효율성이 증대되는 것을 확인하였다.

• 전기학회논문지
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• 제61권5호
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• pp.777-781
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• 2012

In this paper, an electrical power simulation program was developed to predict the energy consumption of the electrical railway propulsion system, which considered the actual operating conditions of the electric railway vehicles. The developed program was designed to predictable the energy consumption during a virtual driving in the actual route of the virtual railway vehicles equipped with a propulsion system consisting of power conversion equipments and traction motors. In addition, the accuracy verification of the electrical power simulation program for a propulsion system was performed by using a real power consumption data, which was measured during the driving of the railway vehicles in the Gyeongui Line. In conclusion, the electrical power simulation program for a propulsion system was validated throughout a comparative investigation between the simulated values and the experimental values and the energy consumption characteristics of electric railway vehicles on the existing route or the new route will be possible to predict throughout the virtual simulation considering the driving conditions of the electric railway vehicles.

• 전기학회논문지P
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• 제62권4호
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• pp.169-174
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• 2013

Due to the increase of power consumption, it is difficult to construct an accurate prediction model for daily peak power demand. It is very important work to know power demand in next day to manage and control power system. In this research, we develop a daily peak power demand prediction method based on Extreme Learning Machine(ELM) with fast learning procedure. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

• 전기학회논문지P
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• 제63권3호
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• pp.189-194
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• 2014

Due to the increasing of power consumption, it is difficult to construct accurate prediction model for daily peak power demand. It is very important work to know power demand in next day for manager and control power system. In this research, we develop a daily peak power demand prediction method based on hybrid type composed of AR and Neuro-Fuzzy model. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

• 전기학회논문지P
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• 제63권4호
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• pp.307-311
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• 2014

Due to the increasing of power consumption, it is difficult to construct accurate prediction model for daily peak power demand. It is very important work to know power demand in next day for manager and control power system. In this research, we develop a daily peak power demand prediction method considering of characteristics of day of week. The proposed method is composed of liner model based on AR model and nonlinear model based on ELM to resolve the limitation of a single model. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

• ETRI Journal
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• 제30권1호
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• pp.47-58
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• 2008

In sensor networks, analyzing power consumption before actual deployment is crucial for maximizing service lifetime. This paper proposes an instruction-level power estimator (IPEN) for sensor networks. IPEN is an accurate and fine grain power estimation tool, using an instruction-level simulator. It is independent of the operating system, so many different kinds of sensor node software can be simulated for estimation. We have developed the power model of a Micaz-compatible mote. The power consumption of the ATmega128L microcontroller is modeled with the base energy cost and the instruction overheads. The CC2420 communication component and other peripherals are modeled according to their operation states. The energy consumption estimation module profiles peripheral accesses and function calls while an application is running. IPEN has shown excellent power estimation accuracy, with less than 5% estimation error compared to real sensor network implementation. With IPEN's high precision instruction-level energy prediction, users can accurately estimate a sensor network's energy consumption and achieve fine-grained optimization of their software.