• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.1
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• pp.50-56
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• 2014

Accurate midterm load forecasting is essential to preventive maintenance programs and reliable demand supply programs. This paper describes a midterm load forecasting method using autoregressive integrated moving average (ARIMA) model which has been widely used in time series forecasting due to its accuracy and predictability. The various ARIMA models are examined in order to find the optimal model having minimum error of the midterm load forecasting. The proposed method is applied to forecast 104-week load pattern using the historical data in Korea. The effectiveness of the proposed method is evaluated by forecasting 104-week load from 2011 to 2012 by using historical data from 2002 to 2010.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.588-591
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• 2003

This paper presents a new short-term load forecasting system using data mining. Since the electric load has very different pattern according to the day, it definitely gives rise to the forecasting error if only one forecasting model is used. Thus, to resolve this problem, the fuzzy model-based classifier and predictor are proposed for the forecasting of the hourly electric load. The proposed classifier is the multi-input and multi-output fuzzy system of which the consequent part is composed of the Bayesian classifier. The proposed classifier attempts to categorize the input electric load into Monday, Tuesday$\sim$Friday, Saturday, and Sunday electric load, Then, we construct the Takagi-Sugeno (T-S) fuzzy model-based predictor for each class. The parameter identification problem is converted into the generalized eigenvalue problem (GEVP) by formulating the linear matrix inequalities (LMIs). Finally, to show the feasibility of the proposed method, this paper provides the short-term load forecasting example.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.6
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• pp.259-266
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• 2005

Short-term load forecasting algorithm using neural networks and the sensitivity of temperatures in the summer season is proposed. In recent 10 years, many researchers have focused on artificial neural network approach for the load forecasting. In order to improve the accuracy of the load forecasting, input parameters of neural networks are investigated for three training cases of previous 7-days, 14-days, and 30-days. As the result of the investigation, the training case of previous 7-days is selected in the proposed algorithm. Test results show that the proposed algorithm improves the accuracy of the load forecasting.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2004.05a
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• pp.434-436
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• 2004

Load forecasting is essential in the electricity market for the participants to manage the market efficiently and stably. The percentage errors of 24 hourly load forecasting for holidays is relatively large. In this paper, we propose the maximum and minimum load forecasting method for holidays using a fuzz linear regression algorithm. 24 hourly loads are forecasted from the maximum and minimum loads and the 24 hourly normalized values. The proposed algorithm is tested for 24 hourly load forecasting in 1996. The test results show the proposed algorithm improves the accuracy of the load forecasting.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.720-722
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• 2009

Optimal operation of electric power generating plants is very essential for any power utility organization to reduce input costs and possibly the prices of electricity in general. This paper developed models for load forecasting using neural networks approach. This model is tested using actual load data of the Busan and weather data to predict the load of the Busan for one month in advance. The test results showed that the neural network forecasting approach is more suitable and efficient for a forecasting application.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.852-856
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• 2015

Demand side management (DSM) program has been frequently used for reducing the system peak load because it gives utilities and independent system operator (ISO) a convenient way to control and change amount of electric usage of end-use customer. Planning and operating methods are needed to efficiently manage a DSM program. This paper presents a planning method for DSM program. A planning method for DSM program should include an electric load forecasting, because this is the most important factor in determining how much to reduce electric load. In this paper, load forecasting with the temperature stochastic modeling and the sensitivity to temperature of the electric load is used for improving load forecasting accuracy. The proposed planning method can also estimate the required day, hour and total capacity of DSM program using Monte-Carlo simulation. The results of case studies are presented to show the effectiveness of the proposed planning method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.12
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• pp.1657-1660
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• 2013

Short-term load forecasting for Chusok and New Year's consecutive holidays is very difficult, due to the irregular characteristics compared with ordinary weekdays and insufficient holidays historical data. During consecutive holidays of New Year and Chusok, most of industries reduce their operation rates and their electrical load levels. The correlation between businesses' operation rates and their loads during consecutive holidays of New Year and Chusok is analysed and short-term load forecasting algorithm for consecutive holidays considering businesses' operation rates of industries is proposed. Test results show that the proposed method improves the accuracy of short-term load forecasting over fuzzy linear regression method.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.11
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• pp.604-609
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• 2004

This paper suggests a long-term distribution area load forecasting algorithm which offers basic data for distribution planning of power system. To build forecasting model, 4-level hierarchical spatial structure is introduced: System, Region, Area, and Substation. And, each spatial load can be decided proportional to its portion in the higher level. This paper introduces the horizon year loads to improve the forecasting results. And, this paper also introduces an effective load transfer algorithm to improve forecasting stability in case of new or stopped substations. The proposed model is applied to the load forecasting of KEPCO system composed of 16 regions, 85 areas and 761 substations, and the results are compared with those of econometrics model to verify its validity.

• 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.