• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.664-666
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• 2003

본 연구에서는 신 수요관리 기법인 DR(Demand Response) 시스템의 적용 현황을 분석하였다. 현재 전력사에서 사용하고 있는 9개의 프로그램에 대한 특성 및 적용 사례를 조사하고 분석하였다. 또한 DR 프로그램의 전형적인 실행과정을 살펴보고 DR의 효과적인 응용에 필요한 부분인 단기 부하예측의 필요와 이들의 방법에 대해서 조사하였다. 부하 예측을 위해서는 수요자의 부하 정보의 분석이 기반이 된다. 따라서 국내에 DR시스템을 도입할 경우에는 수요자의 부하 정보인 Load Profile에 대한 정보의 분석 시스템의 개발이 선행되어야 할 것으로 판단된다.

• Journal of Digital Convergence
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• v.12 no.3
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• pp.243-248
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• 2014

Recently, solar energy is expanding to combination of computing in real time by tracking the position of the sun to estimate the angle of inclination and make up freshly correcting a part of the solar radiation. Solar power is need that reliably linked technology to power generation system renewable energy in order to efficient power production that is difficult to output predict based on the position of the sun rise. In this paper, we analysis of prediction model for solar power generation to estimate the predictive value of solar power generation in the development of real-time weather data. Photovoltaic power generation input the correction factor such as temperature, module characteristics by the solar generator module and the location of the local angle of inclination to analyze the predictive power generation algorithm for the prediction calculation to predict the final generation. In addition, the proposed model in real-time national weather service forecast for medium-term and real-time observations used as input data to perform the short-term prediction models.

• Proceedings of the KIEE Conference
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• summer
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• pp.648-651
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• 2004

현재 원가반영발전경쟁시장(CBP : Cost Based Pool)에서는 발전사업자의 변동비용에 기초하여 공급곡선을 형성하게 된다. 그래서 공급 곡선에 있어서 는 비교적 불확실성이 덜하다고 할 수 있다. 그러나 양방향입찰시장에서의 가격결정은 발전사업자와 전력구매자의 입찰데이터(bidding data)로 결정되므로 불확실성의 정도가 매우 심해진다. 즉 가격결정에 있어서 입찰데이터는 매우 중요하며 입찰전략에 따라 사업자의 수익이 달라지기 때문이다. 또한 수직통합체제 때와는 달리 설비용량의 증설도 계통의 부하를 충족시키기 위해서가 아니라 각 발전사업자의 수익성을 고려하여 수행된다. 따라서 중장기적으로는 설비용량계획의 불확실성이 존재하고 단기적으로는 각 발전사업자의 수익 극대화를 위한 입찰 전략에 있어서의 불확실성이 존재하게 된다. 이와 같은 상황에서는 과거의 역사적 데이터를 바탕으로 해당시장에서 발전사업자들의 형태를 분석하는 실증적 분석(empirical analysis)이 가장 설득력이 있지만 현재 우리나라의 전력 시장은 CBP 체제이고 TWBP 시장은 열리지도 않았기 때문에 축적된 데이터는 전무하다. 이러한 현실적 여건 때문에 불확실성의 정도는 더욱 심해지고 TWBP 시장에서의 가격을 예측하는 과정에서도 어려움이 더욱 커지게 된다. 따라서 본 연구에서는 가능한 다양한 해외 연구 사례를 참조하여 시장에서의 발전사업자 중장기적(설비), 단기적(입찰전략) 행위를 어떤식으로 모델링하고 시장가격과 어떤 식으로 연결되는지를 분석해보고자 한다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2215-2220
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• 2011

The accurate short-term load forecasting is essential for the efficient power system operation and the system marginal price decision of the electricity market. So far, errors of load forecasting for Chuseok Holiday are very big compared with forecasting errors for the other special days. In order to improve the accuracy of load forecasting for Chuseok Holiday, selection of input data, the daily normalized load patterns and load forecasting model are investigated. The efficient data selection and daily normalized load pattern based on fuzzy linear regression model is proposed. The proposed load forecasting method for Chuseok Holiday is tested in recent 5 years from 2006 to 2010, and improved the accuracy of the load forecasting compared with the former research.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.6
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• pp.259-266
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• 2005

Short-term load forecasting algorithm using neural networks and the sensitivity of temperatures in the summer season is proposed. In recent 10 years, many researchers have focused on artificial neural network approach for the load forecasting. In order to improve the accuracy of the load forecasting, input parameters of neural networks are investigated for three training cases of previous 7-days, 14-days, and 30-days. As the result of the investigation, the training case of previous 7-days is selected in the proposed algorithm. Test results show that the proposed algorithm improves the accuracy of the load forecasting.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.8-15
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• 2020

A forecasting method using deep learning does not have consistent results due to the differences in the characteristics of the dataset, even though they have the same forecasting models and parameters. For example, the forecasting model X optimized with dataset A would not produce the optimized result with another dataset B. The forecasting model with the characteristics of the dataset needs to be optimized to increase the accuracy of the forecasting model. Therefore, this paper proposes novel optimization steps for outlier removal, dataset classification, and a CNN-LSTM-based hyperparameter tuning process to forecast the daily power usage of a university campus based on the hourly interval. The proposing model produces high forecasting accuracy with a 2% of MAPE with a single power input variable. The proposing model can be used in EMS to suggest improved strategies to users and consequently to improve the power efficiency.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.7
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• pp.306-312
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• 2006

Support Vector Machine(SVM), of which the foundations have been developed by Vapnik (1995), is gaining popularity thanks to many attractive features and promising empirical performance. In this paper, we propose a new short-term load forecasting technique based on SVM. We discuss the input vector selection of SVM for load forecasting and analyze the prediction performance for various SVM parameters such as kernel function, cost coefficient C, and $\varepsilon$ (the width of 8 $\varepsilon-tube$). The computer simulation shows that the prediction performance of the proposed method is superior to that of the conventional neural networks.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.11
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• pp.1956-1963
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• 2010

Studies on the development of various energy management programs and real-time bidirectional information infrastructures have been actively conducted to promote the reduction of power demands and CO2 emissions effectively. In the conventional energy management programs, the demand response program that can transition or transfer the power use spontaneously for power prices and other signals has been largely used throughout the inside and outside of the country. For measuring the effect of such demand response program, it is necessary to exactly estimate short-term loads. In this study, the power consumption patterns in both individual and group consumers were analyzed to estimate the exact short-term loads, and the relationship between the actual power consumption and seasonal factors was also analyzed.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.3
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• pp.73-78
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• 2001

BP neural network model and multiple-regression model were composed for forecasting the special-days load. Special-days load was forecasted using that neural network model made use of pattern conversion ratio and multiple-regression made use of weekday-change ratio. This methods identified the suitable as that special-days load of short and long term was forecasted with the weekly average percentage error of 1∼2[%] in the weekly peak load forecasting model using pattern conversion ratio. But this methods were hard with special-days load forecasting of summertime. therefore it was forecasted with the multiple-regression models. This models were used to the weekday-change ratio, and the temperature-humidity and discomfort-index as explanatory variable. This methods identified the suitable as that compared forecasting result of weekday load with forecasting result of special-days load because months average percentage error was alike. And, the fit of the presented forecast models using statistical tests had been proved. Big difficult problem of peak load forecasting had been solved that because identified the fit of the methods of special-days load forecasting in the paper presented.

• Journal of the Korean Society of Propulsion Engineers
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• v.3 no.4
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• pp.1-11
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• 1999

Thermal behavior of spacecraft propulsion system utilizing monopropellant hydrazine ($N_2$$H_4$) is addressed in this paper. Thermal control performance to prevent propellant freezing in spacecraft-operational orbit was test-verified under simulated on-orbit environment. The on-orbit environment was thermally achieved in space-simulation chamber and by the absorbed-heat flux method that implements an artificial heating through to the spacecraft bus panels enclosing the propulsion system. Test results obtained in terms of temperature history of propulsion components are presented and reduced into duty cycles of the avionics heaters which are dedicated to thermal control of those components. The duty cycles are subsequently converted into the electrical power required in the operational orbit. Additionally, cyclic temperature of each component, which was made under thermal-balanced condition of spacecraft, is compared to the acceptable design range and justified from the viewpoint of system verification.