• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.908-912
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• 1996

The yearly load forecasting system has been developed for the economic and secure operation of electric power system. It forecasts yearly peak load and thereafter deduces hourly load using the top-down approach. Relative coefficient model has been applied to estimate peak load of a specific date or a specific day of the week. It is equipped with graphic user interface which enables a user to easily access to the system. Yearly average forecasting error may be reduced to $2{\sim}3$(%) only if we can forecast summer-time temperature correctly.

• Journal of the Korean Solar Energy Society
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• v.39 no.2
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• pp.71-80
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• 2019

Daily ahead forecast is necessary for the electricity balance between load and supply due to the variability renewable energy. Numerical weather prediction is usually employed to produce the solar irradiance as well as electric power forecast for more than 12 hours forecast horizon. UM-LDAPS model is the numerical weather prediction operated by Korea Meteorological Administration and it generates the 36 hours forecast of hourly total irradiance 4 times a day. This study attempts to evaluate the model performance against the in situ measurements at 37 ground stations from January to May, 2013. Relative mean bias error, mean absolute error and root mean square error of hourly total irradiance are averaged over all ground stations as being 8.2%, 21.2% and 29.6%, respectively. The behavior of mean bias error appears to be different; positively largest in Chupoongnyeong station but negatively largest in Daegu station. The distinct contrast might be attributed to the limitation of microphysics parameterization for thick and thin clouds in the model.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.8
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• pp.1019-1023
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• 2018

In this paper sensitivity analysis of temperature on special day electricity demand of land and Jeju Island is performed. The basic electricity demand per 3 hours is defined as electricity demand that reflects the GDP effect without the temperature influence. The temperature sensitivity per 3 hours is calculated through the relationship between special day electricity demand normalized to basic electricity demand and temperature. In the future, forecast error will be improved if the temperature sensitivity per 3 hours is applied to the special day load forecasting.

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.152-155
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• 1991

The analysis and forecast of summer cooling load is one of the major concerns of utility company(KEPCO). In this paper, various methodologies to assess the weather sensitive load are introduced and the cause of remarkable growth of the summer cooling load in the last years are analized. To establish the effective measures to migrate the peak building by the summer cooling, a number of practical institutional policies are offered for future implementation.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1996.11a
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• pp.101-103
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• 1996

This Paper describes Peak load forecasting technique of pole transformers with correlation equation. While customers are demanding safe energy supply, current correlation equation that is used for load management of pole transformers has some problems. To get accurate correlation equation. several correlation equation were examined using past data and nu data collected using the measuring instrument developed for this study. It was recognized that the quadratic equation was the most accurate for peak load forecasting from working electrical energy.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1481-1483
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• 1999

Power distribution system planning for maximum customer satisfaction and system efficiency requires accurate forecast of future demand in service area. Spatial load forecasting method provides a more accurate estimation of both magnitudes and location of future electrical load. This method considers the causes of load growth due to addition of customers and per capita consumption among customers by land use (residential, commercial and industrial). So the land-use study and it's preference for small area is quite important. This paper proposes land-use preference estimation method based on fuzzy logic. Fuzzy logic is applied to computing preference scores for each land-use and by these scores the customer growth is allocated in service area. An simulation example is used to illustrate the proposed method.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.242-244
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• 1999

This paper proposes a novel wavelet transform based algorithm for the seasonal load forecasting. In this paper, Daubechies DB2, DB4 and DB10 wavelet transforms are adopted to predict the seasonal loads and the numerical results reveal that certain wavelet components can effectively be used to identify the load characteristics in electric power systems. The wavelet coefficients associated with certain frequency and time localization are adjusted using the conventional multiple regression method and then reconstructed. In order to forecast the final loads through a four-scale synthesis technique. The outcome of the study clearly indicates that the wavelet transform approach can be used as an attractive and effective means of the seasonal load forecasting.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.559-567
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• 2018

In this paper, we propose a new load forecasting method for smart air conditioning (A/C) based on the modified thermodynamics of indoor temperature and the unbiased finite memory estimator (UFME). Based on modified first-order thermodynamics, the dynamic behavior of indoor temperature can be described by the time-domain state-space model, and an accurate estimate of indoor temperature can be achieved by the proposed UFME. In addition, a reliable A/C load forecast can be obtained using the proposed method. Our study involves the experimental validation of the proposed A/C load forecasting method and communication construction between DR server and HEMS in a test bed. Through experimental data sets, the effectiveness of the proposed estimation method is validated.

• Proceedings of the KIEE Conference
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• summer
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• pp.1352-1354
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• 2004

The accurate analysis on railway traction power system should be carried out a load forecast preferentially. Commonly, it has been performed through Train Performance Simulator (TPS). In the study focused on velocity or location of train, however, the electric power consumption have been computed by converting mechanical power according to given velocity. Therefore, this paper presents a development of a mathematical model for electric load. The proposed load model is expressed as polynomial to reflect the influence of variance of train speed, that is, the model expresses the power as a function of train speed. in this study, method of the least squares method is used to find each coefficient and field test is performed to acquire data, electric power and speed of train in commercial running line.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.29-32
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• 1993

A technique computing short-term load foadcasting is essential for monitoring and controlling power system operation. This paper shows the use of general exponential smoothing to develop an adaptive forecasting system based on observed value of hourly demand. Forecasts of hourly load with lead times of one to twenty-four hours are computed at hourly intervals throughout the week. Standard error for lead times of one to twenty-four hour range from three to four percent average load. Studies are planned to investigate the use of weather influence to increase forecast accuracy.