• 조명전기설비학회논문지
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• 제21권7호
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• pp.61-67
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• 2007

전력수요예측은 전력계통의 운용을 위해 필수적이다. 따라서 다양한 방법이 제시되어 왔으며, 특히 특수일의 수요예측은 평일과 구분되며, 부하 패턴을 축출하기에 충분한 자료 확보가 어려워 예측 오차가 크게 나타난다. 본 논문에서는 특수일의 부하예측 정확도를 개선하기 위해 퍼지 최소자승 선형회귀 모델을 분석한다. 4종류의 퍼지 최소자승 선형회귀 모델에 대해 분석과 사례연구를 통하여 가장 정확한 모델을 제시한다.

• 전기학회논문지
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• 제61권12호
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• pp.1765-1773
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• 2012

The accuracy of load forecast is very important from the viewpoint of economical power system operation. In general, the weekdays' load demand pattern has the continuous time series characteristics. Therefore, the conventional methods expose stable performance for weekdays. In case of special days or weekends, the load demand pattern has the discontinuous time series characteristics, so forecasting error is relatively high. Especially, weekdays near the thanksgiving day and lunar new year's day have the mixed load profile characteristics of both weekdays and weekends. Therefore, it is difficult to forecast these days by using the existing algorithms. In this study, a new load forecasting method is proposed in order to enhance the accuracy of the forecast result considering the characteristics of weekdays and weekends. The proposed method was tested with these days during last decades, which shows that the suggested method considerably improves the accuracy of the load forecast results.

• 전기학회논문지
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• 제65권2호
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• pp.253-256
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• 2016

In this paper, the power system with electric vehicles is analyzed considering the mobility and diffusion rate of electric vehicles in the smart grid environment. In the previous studies, load modeling and load composition rates have been researched and the results are applied to develop a new load model to explain the mobility of electric vehicles which could affect on the power system status such as power flow and stability. The results would be utilized to research and develop power system analysis methods considering movable charging characteristics of electric vehicles including movable discharging characteristics which could be affected by the diffusion progress of electric vehicles.

• 대한전기학회논문지
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• 제35권5호
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• pp.193-198
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• 1986

In this paper, 1-168 hours ahead load prediction algorithm is developed for power system economic weekly operation. Total load is composed of three components, which are base load, week load and weather-sensitive load. Base load and week load are predicted by moving average and exponential smoothing method, respectively. The days of moving average and smoothing constant are optimally determined. Weather-sensitive load is modeled by linear form. The paramiters of weather load model are estimated by exponentially weighted recursive least square method. The load prediction of special day is very tedious, difficult and remains many problems which should be improved. Test results are given for the day of different types using the actual load data of KEPCO.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.698-700
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• 1996

A knowledge based forecasting system for special days has been developed for the economic and secure operation of electric power system. If-then production rules has been adopted in this system to be used in various environmental conditions. Graphic user interfaces enables a user to access easily to the system. The simulation based on the historical data have shown that the forecasting result was improved remarkably when compared to the results from the conventional statistical methods. The forecasting results can be used for power system operational planning to improve security and economy of the power system.

• Journal of Electrical Engineering and Technology
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• 제13권2호
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• pp.591-598
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• 2018

Short term load forecasts complexly affected by socioeconomic factors and weather variables have non-linear characteristics. Thus far, researchers have improved load forecast technologies through diverse techniques such as artificial neural networks, fuzzy theories, and statistical methods in order to enhance the accuracy of load forecasts. Short term load forecast errors for special days are relatively much higher than that of weekdays. The errors are mainly caused by the irregularity of social activities and insufficient similar past data required for constructing load forecast models. In this study, the load characteristics of Lunar New Year's Day holidays well known for the highest error occurrence holiday period are analyzed to propose a load forecast technique for Lunar New Year's Day holidays. To solve the insufficient input data problem, the similarity of the load patterns of past Lunar New Year's Day holidays having similar patterns was judged by Euclid distance. Lunar New Year's Day holidays periods for 2011-2012 were forecasted by the proposed method which shows that the proposed algorithm yields better results than the comprehensive analysis method or the knowledge-based method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1988년도 추계학술대회 논문집 학회본부
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• pp.118-121
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• 1988

Load forecasting is an important issue as for the economic dispatch and there have been many researches which are classfied into two classes, time series method and factor analysis method. But the former is not adaptive for a sudden change of a correlated factor and the latter is not inefficient as the factor estimation is not easy. To make matters worse, both of them are not good for the estimation of special days. It is because the load forecasting is not a problem modeled precisely in mathematics, but a problem requires experience and knowledge those can solve it case by case. In this viewpoint, an expert system is proposed which can use complicated experience of an expert by use of fuzzy decision.

• 대한전기학회논문지:전력기술부문A
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• 제48권4호
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• pp.365-370
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• 1999

This paper describes the Weekly Load Forecasting Expert System(Named WLoFy) which was developed and implemented for Korea Electric Power Corporation(KEPCO). WLoFy was designed to provide user oriented features with a graphical user interface to improve the user interaction. The various forecasting models such as exponential smoothing, multiple regression, artificial nerual networks, rult-based model, and relative coefficient model also have been included in WLofy to increase the forecasting accuracy. The simulation based on historical data shows that the weekly forecasting results form WLoFy is an improvement when compared to the results from the conventional methods. Especially the forecasting accuracy on special days has been improved remakably.

• 대한기계학회논문집A
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• 제31권9호
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• pp.903-909
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• 2007

A fatigue capsule is one of the special capsules to investigate the fatigue characteristics of the nuclear materials during an irradiation test in a research reactor, HANARO. In this study, the performance test and the preliminary fatigue test results by using a cyclic load device newly developed for a fatigue capsule are described. In order to obtain the characteristics such as a realization and a controllability of the periodic wave shape and the relationship between the pressure and the load, a spring and rigid bar specimens are used. The fatigue test for the 316L stainless steel specimen with 1.8mm in diameter and 12.5mm in gage length is also performed under the same conditions as the temperature($550^{\circ}C$) of the specimen during irradiation tests. As a result of the test, the fracture of the specimen occurs at a total of 70,120 cycles(about 12 days), and the displacement in this case is 2.02 mm. It is expected that these results will be used for determining test conditions and a comparison of the in-pile fatigue test results.

• 응용통계연구
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• 제32권1호
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• pp.161-171
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• 2019

일별 최대전력 수요 예측은 국가의 전력 수급운영에 중요한 과제로서 과거부터 다양한 방법들이 끊임없이 연구되어 왔다. 일별 최대전력 수요를 정확히 예측함으로써 발전설비에 대한 일일 운용계획을 작성하고 효율적인 설비 운용을 통해 불필요한 에너지 자원의 소비를 감소하는데 기여할 수 있으며 여름 겨울철 냉난방수요로 인해 발생하는 전력소비 과다로 인한 전력예비율 감소 문제 등에 선제적으로 대비할 수 있는 장점을 가진다. 이러한 일별 최대전력수요 예측을 위하여 본 논문에서는 Seasonal ARIMA, TBATS, Seasonal Reg-ARIMA, NNETAR 모형에 평일, 주말, 특수일에 대한 효과와 온도에 대한 영향을 함께 고려하여 다음날의 일별 최대전력을 예측하는 모형을 연구하였다. 본 논문을 통한 모형들의 예측 성능 평가 결과 요일, 온도를 고려할 수 있는 Seasonal Reg-ARIMA 모형과 NNETAR 모형이 이를 고려할 수 없는 다른 시계열 모형보다 우수한 예측 성능을 나타내었고 그 중 인공신경망을 활용한 NNETAR 모형의 예측 성능이 가장 우수하였다.