• 전기학회논문지
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• 제63권9호
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• pp.1186-1191
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• 2014

Weekly and monthly electric load forecasting are essential for the generator maintenance plan and the systematic operation of the electric power reserve. This paper proposes the weekly maximum electric load forecasting model for 104 weeks with the multiple regression model. Input variables of the multiple regression model are temperatures and GDP that are highly correlated with electric loads. The weekly variable is added as input variable to improve the accuracy of electric load forecasting. Test results show that the proposed algorithm improves the accuracy of electric load forecasting over the seasonal autoregressive integrated moving average model. We expect that the proposed algorithm can contribute to the systematic operation of the power system by improving the accuracy of the electric load forecasting.

• Journal of Electrical Engineering and Technology
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• 제8권5호
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• pp.984-990
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• 2013

This paper develops a new Intelligent Electronic Device (IED), and then presents an application method of a monthly load forecasting algorithm on the smart IEDs. A Multiple Linear Regression (MLR) model implemented with Recursive Least Square (RLS) estimation is established in the algorithm. Case Study proves the accuracy and reliability of this algorithm and demonstrates the practical meanings through designed screens. The application method shows the general way to make use of IED's smart characteristics and thereby reveals a broad prospect of smart function realization in application.

• 대한산업공학회지
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• 제28권1호
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• pp.63-75
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• 2002

To improve the efficiency of the electric power generation, monthly maximum electric power consumptions for a next one year should be forecasted in advance and used as the fundamental input to the yearly electric power-generating master plan, which has a greatly influence upon relevant sub-plans successively. In this paper, we analyze the past 22-year hourly maximum electric load data available from KEPCO(Korea Electric Power Corporation) and select necessary data from the raw data for our model in order to reflect more recent trends and seasonal components, which hopefully result in a better forecasting model in terms of forecasted errors. After analyzing the selected data, we recommend to KEPCO the Winters' multiplicative model with decomposition and exponential smoothing technique among many candidate forecasting models and provide forecasts for the electric power consumptions and their 95% confidence intervals up to December of 1999. It turns out that the relative errors of our forecasts over the twelve actual load data are ranged between 0.1% and 6.6% and that the average relative error is only 3.3%. These results indicate that our model, which was accepted as the first statistical forecasting model for monthly maximum power consumption, is very suitable to KEPCO.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 A
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• pp.82-85
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• 1993

The load forecasting has been essential in planning and operation of power systems. The load composition rata is also needed to analyze power-systems - load flow calculation and system stability. This paper proposes the monthly peak load forecasting methods for load groups in residential class using load composition rate and electric consumption characteristics. The proposed methods were applied to a real-scale power system and the effectiveness was turned out.

• 전기학회논문지
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• 제58권9호
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• pp.1695-1699
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• 2009

This paper analyzed the characteristics of the demand of electric power in Jeju by year, day. For this analysis, this research used the correlation between the changes in the temperature and the demand of electric power in summer, and cleaned the data of the characteristics of the temperatures, using the coefficient of correlation as the standard. And it proposed the algorithm of forecasting the short-term electric power demand in Jeju, Therefore, in the case of summer, the data by each cleaned temperature section were used. Based on the data, this paper forecasted the short-term electric power demand in the exponential smoothing method. Through the forecast of the electric power demand, this paper verified the excellence of the proposed technique by comparing with the monthly report of Jeju power system operation result made by Korea Power Exchange-Jeju.

• 산업경영시스템학회지
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• 제39권1호
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• pp.25-30
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• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

• 한국방재안전학회논문집
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• 제14권2호
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• pp.35-42
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• 2021

본 논문은 전기자동차 충전시설 확충계획에 중요한 요소인 전기자동차 전력 수요량 예측정보를 생산하기 위하여 Exponential Smoothing를 이용하여 전력 수요량 예측 모형을 제안하였다. 모형의 입력자료 구축을 위하여 종속변수로 월별 시군구 전력수요량을 독립변수로 월별 시군구 충전소 보급대수, 월별 시군구 전기자동차 충전소 충전 횟수, 월별 전기자동차 등록대수 자료를 월 단위로 수집하고 수집된 7년간 자료 중 4년간 자료를 학습기간으로 3년간 자료를 검증 기간으로 적용하였다. 전기자동차 전력 수요량 예측 모형의 정확성을 검증하기위하여 통계적 방법인 Exponential Smoothing(ETS), ARIMA모형의 결과와 비교한 결과 ETS, ARIMA 각각의 오차율은 12%, 21%로 본 논문에서 제시한 ETS가 9% 더 정확하게 분석되었으며, 전기자동차 전력 수요량 예측 모형으로써 적합함을 확인하였다. 향후 이 모형을 이용한 전기자동차 충전소 설치 계획부터 운영관리 측면에서 활용될 것으로 기대한다.

• 전기학회논문지
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• 제58권1호
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• pp.1-6
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• 2009

The traditional demand prediction was based on the technique wherein electric power corporations made monthly or seasonal estimation of electric power consumption for each area and subscription type for the next one or two years to consider both seasonally generated and local consumed amounts. Note, however, that techniques such as pricing, power generation plan, or sales strategy establishment were used by corporations without considering the production, comparison, and analysis techniques of the predicted consumption to enable efficient power consumption on the actual demand side. In this paper, to calculate the predicted value of electric power consumption on a short-term basis (15 minutes) according to the amount of electric power actually consumed for 15 minutes on the demand side, we performed comparison and analysis by applying a 15-minute interval prediction technique to the average and that to the time series analysis to show how they were made and what we obtained from the simulations.

• 전기전자학회논문지
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• 제23권4호
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• pp.1373-1380
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• 2019

에너지의 생산 효율성을 증가시키기 위해 최근 스마트그리드 기술 중 지능형 검침 시스템(AMI, advanced metering infrastructure)의 개발이 활발히 진행되고 있다. 전력 소비 데이터를 분석하고 소비 패턴을 예측하는 일은 AMI에서 핵심적인 부분이다. 본 논문에서는 수집된 전력 소비 데이터를 분석하고 발생할 수 있는 오류들을 정리하였으며 소비 패턴을 월별로 k-means 군집화 알고리즘을 사용하여 분석하였다. 또한 deep neural network를 이용하여 소비 패턴을 예측하였는데, 가구별 하루 전력 사용량 예측의 어려움을 극복하기 위하여 전력 사용량을 100개의 군집으로 분류하여 이 군집의 하루 평균으로 다음날 군집의 평균을 예측하였다. 실제 AMI에서의 전력 데이터를 사용하여 오류들을 분석하였으며 군집화 방법을 도입하여 성공적으로 전력 소비 예측이 가능하였다.

• 한국물환경학회지
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• 제20권4호
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• pp.333-338
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• 2004

This study was carried out to investigate the generation and discharge characteristics of non-point pollutants from farmlands in Nak-dong river basin. Annual unit generation load of nitrogen and phosphorus by fertilization in the test paddy field was almost similar to those calculated by the fertilization standards of district agricultural technology center, but it was extremely higher in case of the test dry field. By comparing annual total generation load of nutrients from fertilization to the data of fertilizer marketing, the accurate forecasting of generation load of pollutants was achieved by marketing data. The annual total discharge ratio of nutrients through infiltration and overflow from the farmland of the test paddy field were 9.5% and 1.1%, respectively, and those in the test dry field were 22.0% and 0.1%, respectively. The monthly discharge load of nutrients were shown the highest proportioned to the discharge load from lands, but it showed higher in phosphorus, which was caused by the intermittent discharge of phosphorus accumulated in drainage.