• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1088-1095
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• 2015

In most cases, the use of electric power is associated with the economic scale of a nation closely. Thus, the electric power load forecasting plays an important role for the national economic plan. This paper deals with the design method for the electric power load forecasting system. In this paper, RCR-MA data processing, which can make the complex properties of the original data form simple, is proposed. Next, IT2TSK FLS, which can reflect the uncertainty of data more than T1TSK FLS, is applied. Consequently, the structural advantage of the proposed system can improve the forecasting accuracy, and is verified by using two types of electric power data.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.4
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• pp.233-237
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• 2003

An accurate load forecasting is essential for economics and stability power system operation. Due to high relationship between the electric power load and the electric power price, the participants of the competitive power market are very interested in load forecasting. The percentage errors of load forecasting for holidays is relatively large. In order to improve the accuarcy of load forecasting for holidays, this paper proposed load forecasting method for holidays using a fuzzy least squares linear regression algorithm. The proposed algorithm is tested for load forecasting for holidays in 1996, 1997, and 2000. The test results show that the proposed algorithm is better than the algorithm using fuzzy linear regression.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.832-837
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• 2015

The group method of data handling (GMDH) algorithm has proven to be a powerful and effective way to extract rules or polynomials from an electric load pattern. However, because it is nonstationary, the load pattern needs to be decomposed using a discrete wavelet transform. In addition, if a load pattern has a complicated curve pattern, GMDH should use a higher polynomial, which requires complex computing and consumes a lot of time. This paper suggests a method for short-term electric load forecasting that uses a wavelet transform and a GMDH algorithm. Case studies with the proposed algorithm were carried out for one-day-ahead forecasting of hourly electric loads using data during the years 2008-2011. To prove the effectiveness of our proposed approach, the results were evaluated and compared with those obtained by Holt-Winters method and artificial neural network. Our suggested method resulted in better performance than either comparison group.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1522-1522
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• 1999

This paper proposes the methods of short-term load forecasting using Kohonen neural networks and back-propagation neural networks. Historical load data is divided into 5 patterns for the each seasonal data using Kohonen neural networks and using these results, load forecasting neural network is used for next day hourly load forecasting. Normal days and holidays are forecasted. For load forecasting in summer, max-, and min-temperature data are included in neural networks for a better forecasting accuracy. To show the possibility of the proposed method, it was tested with hourly load data of Korea Electric Power Corporation. (1993-1997)

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.6
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• pp.934-939
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• 2016

This paper made short-term electric load forecasting by using temperature data at three-hour intervals (9am, 12pm, 3pm, and 6pm) provided by the Korea Meteorological Administration (KMA). In addition, the electric power pattern was created using existing electric power data, and temperature sensitivity was derived using temperature and electric power data. We made power load forecasting program using LabVIEW, a graphic language.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.8
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• pp.1137-1144
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• 2015

Accurate and robust load forecasting model is very important in power system operation. In case of short-term electric load forecasting, its result is offered as an standard to decide a price of electricity and also can be used shaving peak. For this reason, various models have been developed to improve forecasting accuracy. In order to achieve accurate forecasting result for summer season, this paper proposes a forecasting model using corrected effective temperature based on Heat Index and CDH data as inputs. To do so, we establish polynomial that expressing relationship among CDH, load, temperature. After that, we estimate parameters that is multiplied to each of the terms using PSO algorithm. The forecasting results are compared to Holt-Winters and Artificial Neural Network. Proposing method shows more accurate by 1.018%, 0.269%, 0.132% than comparison groups, respectively.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.2
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• pp.67-78
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• 2019

Accurate electric load forecasting is very important in the efficient operation of the smart grid. Recently, due to the development of IT technology, many works for constructing accurate forecasting models have been developed based on big data processing using artificial intelligence techniques. These forecasting models usually utilize external factors such as temperature, humidity and historical electric load as independent variables. However, due to diverse internal and external factors, historical electrical load contains many missing data, which makes it very difficult to construct an accurate forecasting model. To solve this problem, in this paper, we propose a random forest-based missing data recovery scheme and construct an electric load forecasting model based on multilayer perceptron using the estimated values of missing data and external factors. We demonstrate the performance of our proposed scheme via various experiments.

• 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.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.495-497
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• 1992

Hourly load forecasting has become indispensable for practical simulation of electric power system as the system become larger and more complicated. To forecast the future hourly load the cyclic behavior of electric load which follows seasonal weather, day or week and office hours is to be analyzed so that the trend of the recent behavioral change can be extrapolated for the short term. For the long term, on the other hand, the changes in the infra-structure of each electricity consumer groups should be assessed. In this paper the concept and process of hourly load forecasting for hourly load is introduced.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.2
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• pp.149-154
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• 2013

Short term load forecasting for electric power demand is essential for stable power system operation and efficient power market operation. High accuracy of the short term load forecasting can keep the power system more stable and save the power market operation cost. We propose an optimal coefficient selection method for exponential smoothing model in short term load forecasting on weekdays. In order to find the optimal coefficient of exponential smoothing model, load forecasting errors are minimized for actual electric load demand data of last three years. The proposed method are verified by case studies for last three years from 2009 to 2011. The results of case studies show that the average percentage errors of the proposed load forecasting method are improved comparing with errors of the previous methods.