• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1267-1269
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• 1999

This paper studies on arrangement of load correlation coefficient for advanced load management. To accurate load correlation coefficient, we used two real factors, electrical energy(kWh) and peak load current of pole transformers, acquired by measuring instrument. Out of several correlation equations, we find that the quadratic equation is the most accurate to express peak load current and working electrical energy. If the data is located in the outside of ${\pm}3{\sigma}$ it is discarded. For load management, we rearranged load correlation coefficient considering +2${\sigma}$ at load correlation equation. Comparing conventional load correlation coefficient with rearranged one, we can get the result of error reduced and it is adjacent to the actual data. It will be used peak load forecasting from working electrical energy and we are able to prevent from the damaging of pole transformer due to overload.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1468-1471
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• 1999

This paper presents a method of the regional long-term distribution planning considering economic indicator with the assumption that energy demands proportionally increases with the economic indicators. For the practical distribution planning, it is necessary to regional load forecasting, distribution substation planning, distribution feeder planning. Accordingly, in this paper, after performing regional load forecasting considering economic indicator, it is performed distribution substation planning and distribution feeder planning in order by using this result. For accurate distribution planning, it is very important to scrutinize the correlation among the regional electric power demands, economic indicator and other characteristics because distribution planning results may vary depending on many different factors such as electric power demands, gross products, social trend and so on. In this paper, various steps microscopically and macro scopically are used for the regional long-term distribution planning in order to increase the accuracy and practical use of the results

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.161-171
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• 2019

Varied methods have been researched continuously because the past as the daily maximum electricity demand expectation has been a crucial task in the nation's electrical supply and demand. Forecasting the daily peak electricity demand accurately can prepare the daily operating program about the generating unit, and contribute the reduction of the consumption of the unnecessary energy source through efficient operating facilities. This method also has the advantage that can prepare anticipatively in the reserve margin reduced problem due to the power consumption superabundant by heating and air conditioning that can estimate the daily peak load. This paper researched a model that can forecast the next day's daily peak load when considering the influence of temperature and weekday, weekend, and holidays in the Seasonal ARIMA, TBATS, Seasonal Reg-ARIMA, and NNETAR model. The results of the forecasting performance test on the model of this paper for a Seasonal Reg-ARIMA model and NNETAR model that can consider the day of the week, and temperature showed better forecasting performance than a model that cannot consider these factors. The forecasting performance of the NNETAR model that utilized the artificial neural network was most outstanding.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.177-181
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• 2009

Developing u-Public facilities for application u-City is to combine both the state-of-the art of the construction and ubiquitous computing and must be flexibly comprised of the facilities for the basic service of the building such as air conditioning, heating, lighting and electric equipments to materialize a new format of spatial planning and the public facilities inside or outside. Accordingly, in this paper we suggested the time pattern system for predicting the most basic power system loads for the basic service. To application the tim e pattern we applied SOM algorithm and k-means method and then clustered the data each weekday and each time respectively. The performance evaluation results of suggestion system showed that the forecasting system better the ARIMA model than the exponential smoothing method. It has been assumed that the plan for power supply depending on demand and system operation could be performed efficiently by means of using such power load forecasting.

• Proceedings of the KIEE Conference
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• 2002.11b
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• pp.41-43
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• 2002

The contracted electric power and the demand factor of customers are used to predict the peak load in distribution transformers. The conventional demand factor was determined more than ten years ago. The contracted electric power and power demand have been increased. Therefore, we need to prepare the novel demand factor that appropriates at present. In this paper, we modify the demand factor to improve the peak load prediction of distribution transformers. To modify the demand factor, we utilize the 169 data acquisition devices for sample distribution transformers in winter, spring summer. And, the peak load currents were measured by the case studies using the actual load data, through which we verified that the proposed demand factors were correct than the conventional factors. A newly demand factor will be used to predict the peak load of distribution transformers.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.34 no.6
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• pp.220-229
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• 1985

This paper proposes a new optimal unit commitment algorithm for the rational operation of electric power systems. Especially, the algorithm is improved by considering transmission line capacity limits and load forecasting uncertainty with the consideration of the participation factors of each units, so that the method becomes more reliable and flexible one. The transmission losses are considered by using updated penalty factors obtained from the constant matrixes of the fast decoupled load flow method, the system loads are distributed at each buses, and the several necessary operational constraints are also considered for the purpose of presenting a more practicable scheme. Finally, the effectiveness of the proposed algorithm has been demonstrated by applying to the 23-bus model system.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.32 no.6
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• pp.191-198
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• 1983

This paper describes the aplication of different model which can be used for short-term load prediction. The model is based on Bohlin's approach to first develop a load profile model representing the nominal load component and the Box-Jenkins approach is used to predict residuals. An on-line algorithm using Kalman Filter and Time Series is implemented for and hour-ahead prediction. In the Kalman Filter system equation and measurement equation were fixed and parameters of Time Series were varied week after week. A set of data for Korea Electric Power Corporation from April to June 1981 was used for the evaluation of the model. As the result of this simulation 1.2% rms error was acquired.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.186-192
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• 1995

본 연구에서는 퍼지 클러스터링 알고리즘과 변수선택 방법을 이용하여 모델의 구조 동정을 행하고, 신경회로망의 Back-propagation 학습방법을 이용하여 파라메터동정을 행하 는 새로운 퍼지모델링 알고리즘을 제안하였다. 실제 데이터를 이용하여 전력부하예측시스템 을 설계하였으며 그 결과 타당성을 입증하였다.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.8 no.3
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• pp.44-53
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• 1994

전력 부하의 예측은 산업 활동에 있어 전력의 안정적인 공급과 생산비의 절감을 위해 대단히 중요하다. 전력 부하의 예측 방법들이 많이 연구되고 있으나 기존의 방법들은 수학적으로 복잡하고 계산 시간이 많이 소요되는 단점을 갖고 있다. 본 연구에서는 최적 규칙수를 구하는 클러스터링 알고리즘과 데이터를 2분하여 설계한 변수 선택 방법을 이용하여 모델을 간략화하는 알고리즘을 제안하였으며, CMAC을 이용한 데이터의 양·부 판별 알고리즘을 이용하므로써 노이즈의 영향을 최소화 하였다. 제안된 알고리즘을 이용하여 전력부하예측 시스템을 설계하고 분석한 결과 그 타당성을 입증하였다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.2
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• pp.331-337
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• 2015

The power flow analysis of electrified railway is required complicated calculation, because of variable load. Train runs trough rail supplied by electric power therefore, the load value in electrified railway system fluctuates along time. The power flow algorithm in electrified railway system is different from general power system, and the power flow simulation is peformed by the particular simulation software. Powerail is simulation software for analysis of traction power supply system developed by KRRI, in 2008. This consists of load forecasting module, including TPS and time scheduling, and power flow module. This software was verified by measured current under normal feeding condition, however, has not been verified by voltage on the condition of extended feeding. This paper presents the verification of PowerRail based on voltage drop under extended feeding condition. This is performed by comparing simulation result with field test. Field test and simulation is done in commercial railway line.