• Journal of the Institute of Convergence Signal Processing
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• v.5 no.1
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• pp.53-59
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• 2004

In case of load forcasting the most important problem is to deal with the load of special days. According this paper presents forecasting method for speaial days peak load by neural networks model. by means of neural networks mothod using the historical past special- days load data, special-days load was directly forecasted, and forecasting % error showed good result as 1∼2% except vacation season in summer Consequently, it is capable of directly special days load, With the models, precision of forecasting was brought satisfactory result. When neural networks was compared with the orthogonal polynomials models at a view of the results of special-days load forecasting, neural networks model which used pattern conversion ratio was more effective on forecasting for special-days load. On the other hand, in case of short special-days load forecasting, both were valid.

• 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
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• v.62 no.4
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• pp.482-488
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• 2013

Load forecasting is essential to the economical and the stable power system operations. In general, the forecasting days can be classified into weekdays, weekends, special days and special light-load periods in short-term load forecast. Special light-load periods are the consecutive holidays such as Lunar New Years holidays, Korean Thanksgiving holidays and summer special light-load period. For the weekdays and the weekends forecast, the conventional methods based on the statistics are mainly used and show excellent results for the most part. The forecast algorithms for special days yield good results also but its forecast error is relatively high than the results of the weekdays and the weekends forecast methods. For summer special light-load period, none of the previous studies have been performed ever before so if the conventional methods are applied to this period, forecasting errors of the conventional methods are considerably high. Therefore, short-term load forecast for summer special light-load period have mainly relied on the experience of power system operation experts. In this study, the trends of load profiles during summer special light-load period are classified into three patterns and new forecast algorithms for each pattern are suggested. The proposed method was tested with the last ten years' summer special light-load periods. The simulation results show the excellent average forecast error near 2%.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.295-297
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• 2000

In case of power demand forecasting the most important problem is to deal with the load of special-days, Accordingly, this paper presents a method that forecasting special-days load with regression models and neural networks. Special-days load in summer season was forecasted by the multiple regression models using weekday change ratio Neural networks models uses pattern conversion ratio, and orthogonal polynomial models was directly forecasted using past special-days load data. forecasting result obtains % forecast error of about $1{\sim}2[%]$. Therefore, it is possible to forecast long and short special-days load.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1115-1120
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• 2016

This paper presents the daily load forecasting for special days considering the rate of operation of industrial consumers. The authors analyzed the power consumption pattern for both the special and ordinary days according to the contract power classification of industrial consumers, and selected 400~600 specific consumers for which the rates of operation during special days are needed. Load forecasting for 2014 special days considering the rate of operation of industrial consumers showed a noticeable improvement on forecasting error of daily peak demand, which proved the effectiveness of the survey for the rates of operation during special days of industrial consumers.

• 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 Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.5
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• pp.94-102
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• 2005

This paper performs analysis the load patterns for the all the special days and studies the change of the load patterns for the last 15 years using Expert system based on the load record and the weather condition record. The Expert system is one of the four major load forecasting methods of the power system And it is used for forecasting. loads of the special days based on the Know-how of the load forecasting Experts. After the author simulates the load forecasting using hourly relative factors of the load patterns based on the past load records, there is considerable improved effect. The average errors of past 5 days load forecasting of lunar New Year's Day (year 2002 and 2003) is $3.23{[\%]}$. Using the new method the author forecast loads of the lunar new year's days (the year 2005) and it shows only $1.78{[\%]}$ error. A field manual for the load forecast can be made using proposed method. The authors expect this article could give a guidance to those who wish to be load forecast expert.

• Journal of Industrial Technology
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• v.18
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• pp.379-384
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• 1998

Conventional short-term load forecasting techniques have limitation in their use on holidays due to dissimilar load behaviors of holidays and insufficiency of pattern data. Thus, a new short-term load forecasting method for special days in anomalous load conditions is proposed in this paper. The proposed method uses two Artificial Neural Networks(ANN); one is for the estimation of load curve, and the other is for the estimation of minimum and maximum value of load. The forecasting procedure is as follows. First, the normalized load curve is estimated by ANN. At next step, minimum and maximum values of load in a special day are estimated by another ANN. Finally, the estimate of load in a whole special day is obtained by combining these two outputs of ANNs. The proposed method shows a good performance, and it may be effectively applied to the practical situations.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.850-853
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• 1998

This paper proposes a new short-term load forecasting method for special day, such as Public holidays, consecutive holidays, and days before and after holidays. when the load curves are quite different from those of normal weekdays. In this paper, two Artificial Neural Network(ANN) systems are applied to short-term load forecasting for spacial days in anomalous load conditions.

• 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.