• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.137-146
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• 2011

In order to support increased consumer awareness regarding energy consumption, we present new ways of monitoring and predicting with energy in electric appliances. The proposed system is a design of a common electrical power outlet called smart plug that measures the amount of current passing through current sensor at 0.5 second. To acquire data for training and testing the proposed neural network, weather parameters used include average temperature of day, min and max temperature, humidity, and sunshine hour as input data, and power consumption as target data from smart plug. Using the experimental data for training, the neural network model based on Back-Propagation algorithm was developed. Multi layer perception network was used for nonlinear mapping between the input and the output data. It was observed that the proposed neural network model can predict the power consumption quite well with correlation coefficient was 0.9965, and prediction mean square error was 0.02033.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.193-203
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• 2016

This paper forecasts electricity demand as a critical element of a demand management system in Smart Grid environment. We present a prediction method of using a combination of predictive values by time series clustering. Periodogram-based normalized clustering, predictive analysis clustering and dynamic time warping (DTW) clustering are proposed for time series clustering methods. Double Seasonal Holt-Winters (DSHW), Trigonometric, Box-Cox transform, ARMA errors, Trend and Seasonal components (TBATS), Fractional ARIMA (FARIMA) are used for demand forecasting based on clustering. Results show that the time series clustering method provides a better performances than the method using total amount of electricity demand in terms of the Mean Absolute Percentage Error (MAPE).

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.144-146
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• 2022

차량 환경에서 발생하는 계산 집약적인 태스크가 증가하면서 모바일 엣지 컴퓨팅(MEC, Mobile Edge Computing)의 필요성이 높아지고 있다. 하지만 지상에 존재하는 MEC 서버는 출퇴근 시간과 같이 태스크가 일시적으로 급증하는 상황에 유동적으로 대처할 수 없으며, 이러한 상황을 대비하기 위해 지상 MEC 서버를 추가로 설치하는 것은 자원의 낭비를 불러온다. 최근 이 문제를 해결하기 위해 UAV(Unmanned Aerial Vehicle)기반 MEC 서버를 추가로 사용해 엣지 서비스를 제공하는 연구가 진행되고 있다. 그러나 UAV MEC 서버는 지상 MEC 서버와 달리 한정적인 배터리 용량으로 인해 서버 간 로드밸런싱을 통해 에너지 사용량을 최소화 하는 것이 필요하다. 본 논문에서는 UAV MEC 서버의 에너지 사용량을 고려한 마이그레이션 기법을 제안한다. 또한 GRU(Gated Recurrent Unit) 모델을 활용한 트래픽 예측을 바탕으로 한 마이그레이션을 통해 지연시간을 최소화할 수 있도록 한다. 제안 시스템의 성능을 평가하기 위해 MEC의 마이그레이션 시점을 결정하는 기준점와 차량의 밀도에 따라 실험을 진행하고, 서버의 로드 편차, UAV MEC 서버의 에너지 사용량 그리고 평균 지연 시간 측면에서 성능을 분석한다.

• Journal of the Korea Convergence Society
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• v.8 no.1
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• pp.19-23
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• 2017

Energy is a vital resource for the economic growth and the social development for any country. As the industry becomes more sophisticated and the economy more grows, the electricity demand is increasing. So forecasting electricity demand is an important for electricity suppliers. Forecasting electricity demand makes it possible to distribute electricity demand. As the market for Negawatt market began to grow in Korea from 2014, the prediction of electricity consumption demand becomes more important. Moreover, power consumption forecasting provides a way for demand management to be directly or indirectly participated by consumers in the electricity market. We use Genetic Algorithms to predict the energy demand of the fishing industry in Jeju Island by using GDP, per capita gross national income, value add, and domestic electricity consumption from 1999 to 2011. Genetic Algorithm is useful for finding optimal solutions in various fields. In this paper, genetic algorithm finds optimal parameters. The objective is to find the optimal value of the coefficients used to predict the electricity demand and to minimize the error rate between the predicted value and the actual power consumption values.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.15 no.4
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• pp.287-293
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• 2003

This study examined the temperature-dependent regression model of energy consumption based on various measuring period. The methodology employed was to construct temperature-dependent linear regression model of daily energy consumption from one day to three months data-sets and to compare the annual heating energy consumption predicted by these models with actual annual heating energy consumption. Heating energy consumption from a building in Daejon was examined experimentally. From the results, predicted value based on one day experimental data can have error over 100%. But predicted value based on one week experimental data showed error over 30%. And predicted value based on over three months experimental data provides accurate prediction within 6% but it will be required very expensive.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.265-268
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• 2008

임베디드 시스템에서의 소프트웨어 비중과 규모의 지수적 증가로 인해 관련 개발 프로젝트의 경우 많은 개발 지원도구를 활용한 개발활동이 이루어 지고 있다. 하지만 각 개발 조직간 상이한 개발 지원도구의 사용과 관리로 인해, 협업 개발체제에 문제가 발생하고 있으며, 단위 개발조직의 개발 지원도구의 추가 소요량 산정시 정량적인 데이터에 근거하지 않은 부정확한 구매 예측으로 인해 많은 개발 예산의 낭비가 발생하고 있다. 이에 본 연구에서는 개발 조직간 개발 효율성을 높이기 위한 효율적인 인프라 환경을 구축하기 위해, 통합 도메인 환경하에 개발 지원도구의 일원화된 관리와 개발조직간의 라인센스 사용량 측정을 위한 모니터링 시스템 개발로, 개발 지원 도구 추가 소요 발생시 정확한 소요량 예측과, 사용량에 근거한 공정한 비용 분배 절차를 제시하고자 한다.

• Proceedings of the KIEE Conference
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• 2008.09a
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• pp.83-86
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• 2008

국내 한국진력에서 관리 운영하고 있는 전자식 전력량계는 약 55만 여대이며, 이중 약 15만 여대가 고안용 전자식 전력량계로 2000년대 초반부터 원격검침을 통해 전력사용량을 측정하고 고객 측에 대해서는 역률과 누적전력량 등의 기타 부가서비스를 제공하고 있다. 전력량계와 원격통신을 이용한 전력부가서비스는 전력회사의 입장에서는 고객의 부하사용에 대한 다양한 정보를 효율적으로 수집하여 피크부하 제어 등의 수요관리를 효과적으로 시행할 수 있고 고객의 입장에서는 과거는 물론 미래의 사용량을 예측하고 기타 전력사용패턴 등을 관찰함으로서 고객의 능동적인 참여로 전력사용량을 절약하는 효과를 가져 올 수 있다. 현 시점에서의 국내 전력부가서비스의 활용수준은 초기단계로 매우 기본적인 정보에 한해서 활용이 되고 있다. 이에 본 논문에서는 기존의 국내외 관련 연구사례와 적용사례, 그리고 시행현황의 조사 분석을 통해 향후의 국내 전력부가서비스 적용전망과 발전방향을 모색해 보고자 한다.

• Korean Journal of Construction Engineering and Management
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• v.12 no.6
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• pp.110-119
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• 2011

Global warming caused by excessive greenhouse gas emission is causing climate change all over the world. In Korea, greenhouse gas emission from residential buildings accounts for about 10% of gross domestic emission. Also, the number of deteriorated multi-family housing complexes is increasing. Therefore, the goal of this research is to establish the bases to manage energy consumption continuously and methodically during MR&R period of multi-family housings. The research process and methodologies are as follows. First, research team collected the data on project characteristics and energy consumption of multi-family housing complexes in Seoul. Second, an ontology-based breakdown structure was established with some primary characteristics affecting the energy consumption, which were selected by statistical analysis. Finally, a predictive model of energy consumption was developed based on the ontology-based breakdown structure, with application of CBR, ANN, MRA and GA. In this research, PASW (Predictive Analytics SoftWare) Statistics 18, Microsoft EXCEL, Protege 4.1 were utilized for data analysis and prediction. In future research, the model will be more continuous and methodical by developing the web-base system. And it has facility manager of government or local government, or multi-family housing complex make a decision with definite references regarding moderate energy consumption.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.79-85
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• 2019

In this paper, we propose a short-term power forecasting method by applying Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network to Internet of Things (IoT) power meter. We analyze performance based on real power consumption data of households. Mean absolute error (MAE), mean absolute percentage error (MAPE), mean percentage error (MPE), mean squared error (MSE), and root mean squared error (RMSE) are used as performance evaluation indexes. The experimental results show that the GRU-based model improves the performance by 4.52% in the MAPE and 5.59% in the MPE compared to the LSTM-based model.

• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.