• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.2
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• pp.98-103
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• 2002

In case of power demand forecasting, the most important problems are to deal with the load of special-days. Accordingly, this paper presents the method that forecasting long (the Lunar New Year, the Full Moon Festival) and short(the Planting Trees Day, the Memorial Day, etc) special-days peak load using neural networks and regression models. long and short special-days peak load forecast by neural networks models uses pattern conversion ratio and four-order orthogonal polynomials regression models. There are using that special-days peak load data during ten years(1985∼1994). In the result of special-days peak load forecasting, forecasting % error shows good results as about 1 ∼2[%] both neural networks models and four-order orthogonal polynomials regression models. Besides, from the result of analysis of adjusted coefficient of determination and F-test, the significance of the are convinced four-order orthogonal polynomials regression models. When the neural networks models are compared with the four-order orthogonal polynomials regression models at a view of the results of special-days peak load forecasting, the neural networks models which uses pattern conversion ratio are more effective on forecasting long special-days peak load. On the other hand, in case of forecasting short special-days peak load, both are valid.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.6
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• pp.358-363
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• 2004

Due to the weather sensitivity of the power load, it is difficult to forecast accurately the peak power load of summer season. We improve the accuracy of the load forecasting considering weather condition. We introduced the sensitivity of temperature and proposed an improved forecasting algorithm. The proposed algorithm shows that the error of the load forecasting is 1.5%.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.199-200
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• 2011

Load forecasting is very important for power system analysis and planning. This paper suggests yearly load forecasting of considering weekly normalization and seasonal load characteristics. Each weekly peak load is normalized and the average value is calculated. The new hourly peak load is seasonally collected. This method was used for yearly load forecasting. The results of the actual data and forecast data were calculated error rate by comparing.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.96-102
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• 2018

For efficient operating strategy of electric power system, forecasting of daily peak electric load is an important but difficult problem. Therefore a daily peak electric load forecasting system using a neural network and fuzzy system is presented in this paper. First, original peak load data is interpolated in order to overcome the shortage of data for effective prediction. Next, the prediction of peak load using these interpolated data as input is performed in parallel by a neural network predictor and a fuzzy predictor. The neural network predictor shows better performance at drastic change of peak load, while the fuzzy predictor yields better prediction results in gradual changes. Finally, the superior one of two predictors is selected by the rules based on rough sets at every prediction time. To verify the effectiveness of the proposed method, the computer simulation is performed on peak load data in 2015 provided by KPX.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.6
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• pp.35-42
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• 2001

Since daily peak load forecasted data are essential to economic operation and power monitor, the technique of accurate forecasting is needled. The chief advantage of forecasting technique using neural network and fuzzy theory is high accuracy and operative implicity but the loaming time is long, and it makes large forecasting error when the load changes rapidly. This paper has resented a new forecasting technique to improve those faults and the forecasting technique prove to be valid by forcasted results.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.10
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• pp.453-459
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• 2002

This paper proposes an improved algorithm of the daily peak load forecasting for Korean Thanksgiving Day and the Lunar New Year's day. So far, many studies on the short-term load forecasting have been made to improve the accuracy of the load forecasting. However, the large errors of the load forecasting occur i case of Korean Thanksgiving Day and the Lunar New Year's Day. In order to reduce the errors of the load forecasting, the fuzzy linear regression method is introduced and a good selection method of the past load pattern is presented. Test results show that the proposed algorithm improves the accuracy of the load forecasting.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.3
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• pp.137-143
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• 2014

Demand Controller is a load control device that monitor the current power consumption and calculate the forecast power to not exceed the power set by consumer. Accurate demand forecasting is important because of controlling the load use the way that sound a warning and then blocking the load when if forecasted demand exceed the power set by consumer. When if consumer with fluctuating power consumption use the existing forecasting method, management of demand control has the disadvantage of not stable. In this paper, load forecasting of the unit of seconds using the Exponential Smoothing Methods, ARIMA model, Kalman Filter is proposed. Also simulation of load forecasting of the unit of the seconds methods and existing forecasting methods is performed and analyzed the accuracy. As a result of simulation, the accuracy of load forecasting methods in seconds is higher.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.3
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• pp.109-117
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• 2002

High accuracy of the load forecasting for power systems improves the security of the power system and generation cost. However, the forecasting problem is difficult to handle due to the nonlinear and the random-like behavior of system loads as well as weather conditions and variation of economical environments. So far. many studies on the problem have been made to improve the prediction accuracy using deterministic, stochastic, knowledge based and artificial neural net(ANN) method. In the conventional load forecasting method, the load forecasting maximum error occurred for the holidays on Saturday and Monday. In order to reduce the load forecasting error of the daily peak load for the holidays on Saturday and Monday, fuzzy concept and linear regression theory have been adopted into the load forecasting problem. The proposed algorithm shows its good accuracy that the average percentage errors are 2.11% in 1996 and 2.84% in 1997.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.5
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• pp.718-723
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• 2016

The temperature changes are very important in improving the accuracy of the load forecasting during the summer. It is because the cooling load in summer contribute to the increasing of the load. This paper proposes a weekend load forecasting algorithm using the temperature change characteristic in a summer of Jeju. The days before and after weekends in Jeju, when the load curves are quite different from those of normal weekdays. The temperature change characteristic are obtained by using weekends peak load and high temperature data. And load forecasted based on the sensitivity between unit temperature changes and load variations. Load forecast data with better accuracy are obtained by using the proposed temperature changes than by using the ordinary daily peak load forecasting. The method can be used to reduce the error rate of load forecast.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.39-49
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• 2013

This paper presents a method of improving the performance of a day-ahead 24-h load curve and peak load forecasting. The next-day load curve is forecasted using radial basis function (RBF) neural network models built using the best design parameters. To improve the forecasting accuracy, the load curve forecasted using the RBF network models is corrected by the weighted sum of both the error of the current prediction and the change in the errors between the current and the previous prediction. The optimal weights (called "gains" in the error correction) are identified by differential evolution. The peak load forecasted by the RBF network models is also corrected by combining the load curve outputs of the RBF models by linear addition with 24 coefficients. The optimal coefficients for reducing both the forecasting mean absolute percent error (MAPE) and the sum of errors are also identified using differential evolution. The proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange. Simulation results reveal satisfactory forecasts: 1.230% MAPE for daily peak load and 1.128% MAPE for daily load curve.