• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.171-177
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• 2021

In terms of distribution planning, accurate electric load prediction is one of the most important factors. The future load prediction has manually been performed by calculating the maximum electric load considering loads transfer/switching and multiplying it with the load increase rate. In here, the risk of human error is inherent and thus an automated maximum electric load forecasting system is required. Although there are many existing methods and techniques to predict future electric loads, such as regression analysis, many of them have limitations in reflecting the nonlinear characteristics of the electric load and the complexity due to Photovoltaics (PVs), Electric Vehicles (EVs), and etc. This study, therefore, proposes a method of predicting future electric loads on distribution lines by using Machine Learning (ML) method that can reflect the characteristics of these nonlinearities. In addition, predictive models were developed based on actual data collected at KEPCO's existing distribution lines and the adequacy of developed models was verified as well. Also, as the distribution planning has a direct bearing on the investment, and amount of investment has a direct bearing on the maximum electric load, various baseline such as maximum, lowest, median value that can assesses the adequacy and accuracy of proposed ML based electric load prediction methods were suggested.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.9
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• pp.1186-1191
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• 2014

Weekly and monthly electric load forecasting are essential for the generator maintenance plan and the systematic operation of the electric power reserve. This paper proposes the weekly maximum electric load forecasting model for 104 weeks with the multiple regression model. Input variables of the multiple regression model are temperatures and GDP that are highly correlated with electric loads. The weekly variable is added as input variable to improve the accuracy of electric load forecasting. Test results show that the proposed algorithm improves the accuracy of electric load forecasting over the seasonal autoregressive integrated moving average model. We expect that the proposed algorithm can contribute to the systematic operation of the power system by improving the accuracy of the electric load forecasting.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.2
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• pp.315-323
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• 2015

Nowadays, it is expected that mobility loads such as electric railways and electric vehicles will be penetrated gradually and affect on the power system stability by their load characteristics. Various researches have been carried out about electric vehicles for the recent decade though the load of electric railway could be forecasted because of the specified path and timetable, is a field with a long historic background. Some precise 5th polynomial equations are required to analyze the power system stability considering mobility load to be increased in the immediate future while the electric railway dispatching simulator uses load models with constant power and constant impedance for the system analysis. In this paper, seasonal urban railway load models are established as the form of 5th polynomial equations and substation load modeling methods are proposed merging railway station load models and general load models. Additionally, load management effects by the load modeling are confirmed through the case studies, in which seasonal load models are developed for Seoul Subway Line No. 2, Gyeongui Line and Airport Railroad and the substation load change is analyzed according to the railway load change.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.115-123
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• 2021

Through machine learning-based load prediction, it is possible to prevent excessive power generation or unnecessary economic investment by estimating the appropriate amount of facility investment in consideration of the load that will increase in the future or providing basic data for policy establishment to distribute the maximum load. However, in order to secure the reliability of the developed load prediction model in the field, the performance comparison verification between the distribution line load prediction models must be preceded, but a comparative performance verification system between the distribution line load prediction models has not yet been established. As a result, it is not possible to accurately determine the performance excellence of the load prediction model because it is not possible to easily determine the likelihood between the load prediction models. In this paper, we developed a reliability verification system for load prediction models including a method of comparing and verifying the performance reliability between machine learning-based load prediction models that were not previously considered, verification process, and verification result visualization methods. Through the developed load prediction model reliability verification system, the objectivity of the load prediction model performance verification can be improved, and the field application utilization of an excellent load prediction model can be increased.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.12
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• pp.1959-1963
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• 2012

Since electric railway load is variable largely due to starting and braking characteristics as well as various operation patterns, load modeling is not easy but complicated. For this reason, a simple technique for modeling of electric railway load of is required to analyze the AC substation system of electric railway. In this paper, a modeling technique of converter-based electric railway load is proposed and is tested using nonlinear loads on Matlab/Simulink.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.1
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• pp.24-31
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• 2012

This study is designed to predict the overall electric power load, to apply the method of time sharing and to reduce simultaneous load factor of electric power when authorized by user entering demand plans and using schedules into the user's interface for a certain period of time. This is about smart grid, which reduces electric power load through simultaneous load factor of electric power reduction system supervision agent. Also, this study has the following characteristics. First, it is the user interface which enables authorized users to enter and send/receive such data as demand plan and using schedule for a certain period of time. Second, it is the database server, which collects, classifies, analyzes, saves and manages demand forecast data for a certain period of time. Third, is the simultaneous load factor of electric power control agent, which controls usage of electric power by getting control signal, which is intended to reduce the simultaneous load factor of electric power by the use of the time sharing control system, form the user interface, which also integrate and compare the data which were gained from the interface and the demand forecast data of the certain period of time.

• Journal of Advanced Marine Engineering and Technology
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• v.24 no.3
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• pp.106-112
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• 2000

Since the oil crisis, we have been concerned about the energy saving techniques of electric generating systems. As a part of the effort to save energy, automatic electric load sharing device was developed. Usually, ship's electric generating system consists of two or three sets of generator. And, electric generating system is operated as single or parallel operation mode according to the demanded electric power. Therefore, it is important to investigate generators operating mode for the supply of required electric power in the ship in order to decrease the operating cost. So, this paper suggests the method to solve the optimal electric load distribution problem by dynamic programming. And, this thesis indicates that the proposed method is superior to the lagrange multiplier's method in obtaining optimal load distribution solution in the ship's electric generating system.

• KEPCO Journal on Electric Power and Energy
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• v.1 no.1
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• pp.61-65
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• 2015

For effective distribution planning and operation, we need a reliable estimation of operation capacity. But it is difficult to ensure reliability due to the low accuracy of section load data, which is used as a basis in estimating the operation capacity. This paper discusses how to improve the accuracy of section load data by analyzing the existing method of estimating the section load, using statistical techniques to adjust the acquired data, and using the section load estimation algorithm to estimate the section load based on the adjusted data.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.05a
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• pp.397-399
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• 2007

This paper presents multiple regression analysis and data update to improve present spatial electric load forecasting algorithm of the DISPLAN. Spatial electric load forecasting considers a local economy, the number of local population and load characteristics. A Case study is performed for Jeon-Ju and analyzes a trend of the spatial load for the future 20 years. The forecasted information can contribute to an asset management of distribution systems.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1419-1424
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• 2018

Very short-term electric load forecasting is essential for real-time power system operation. In this paper, a very short-term electric load forecasting technique applying the Kalman filter algorithm is proposed. In order to apply the Kalman filter algorithm to electric load forecasting, an electrical load forecasting algorithm is defined as an observation model and a state space model in a time domain. In addition, in order to precisely reflect the noise characteristics of the Kalman filter algorithm, the optimal error covariance matrixes Q and R are selected from several experiments. The proposed algorithm is expected to contribute to stable real-time power system operation by providing a precise electric load forecasting result in the next six hours.