• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.11 no.5
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• pp.62-66
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• 1997

This paper is presented to short-term load forecast algorithm using weekday change ratio. The week periodicity was excluded from weekday change ratio. That was composed with the power demand forecast term of five and multiple regression model of the three form. The precision was good with 2.8[%]. Also the power demand of special day(weekend) of completely difficult forecast case of using the multiple regression model was able to forecast at this paper. Therefore, the forecast precision was enhanced and the reliable forecast model was constructed.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.4
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• pp.511-516
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• 1996

In this paper, weekly maximum power demand forecasting method in consideration of temperature estimation using a time series model was presented. The method removing weekly, seasonal variations on the load and irregularities variation due to unknown factor was presented. The forecasting model that represent the relations between load and temperature which get a numeral expected temperature based on the past 30 years(1961~1990) temperature was constructed. Effect of holiday was removed by using a weekday change ratio, and irregularities variation was removed by using an autoregressive model. The results of load forecasting show the ability of the method in forecasting with good accuracy without suffering from the effect of seasons and holidays. Percentage error load forecasting of all seasons except summer was obtained below 2 percentage. (author). refs., figs., tabs.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.1177-1179
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• 1997

In this paper, short-term power demand forecast using exclusion of week periodicity presented. Week periodicity excluded from weekday change ratio. Forecast term of five and multiple regression model of the three form was composed. Forecast result was good. Therefore, It Could be the power demand forecast of special day(weekend). This method may contribute improvement of forecast accuracy.

• 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.

• Journal of the Korean Home Economics Association
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• v.27 no.1
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• pp.111-126
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• 1989

The aim of the present study is to research into the household work time change and its structure in urban home makers by the choosen eleven studies and KBS's Data 1981, 1983, 1985, 1987. This study were proceeded under some limitations, it is choosen eleven studies that is different region: large city, medium and small town, and the household work's categories of original auther were changed. And KBS's Data was composed of general formation without personal character of home maker: FLC, number of childeren, family type, education, region. Although this study have a certain meaning of implementation, research into the household work time change and its's structure. The major findings of this study can be autlined as follows: (1) Total household work time did'nt so much changed through the choosen eleven studies compared with the last twenty years ago. In the change of each province household work time, time connected with meals and dwelling did not showed consistancy of change. But cloth laundering and mending time of 80's were declined compared with 70's. Family care time of 80's was increased, home management and buying time was declined untill '85, but again increasing trend '87. In choosen eleven studies, the household work time structure of urban home makers can be outlined: time connected with Meals>Family care>Cloth laundering and mending>Dwelling>Home management and Buying. (2) KBS's time-series data were analized as follows: a) Total household work time of '87 was declining gradually in weekday (34 minutes), sat. (41 minutes), sun (1 house and 2 minutes) compared with '81. b) The change of each province household work time: the time of cooking and sewing home management were declining gradually in its Mean time and its ratio of acters. The acter ratio of household worker in '81, '83, '85 was composed Cooking > Cleaning > Laundering > Home management > Buying > Child care > Sewing. In '87 was composed Cooking > Cleaning > Laundering > Buying > Home management > Child care > Sewing. c) The structure of household work time revealed some differences in each year and a day of the week.