• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.119-127
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• 2020

As the electric vehicle (EV) market in South Korea grows, it is required to expand charging facilities to respond to rapidly increasing EV charging demand. In order to conduct a comprehensive facility planning, it is necessary to forecast future demand for electricity and systematically analyze the impact on the load capacity of facilities based on this. In this paper, we design and develop a Long Short-Term Memory (LSTM) neural network model that predicts the daily peak electric load at each charging station using the EV charging data of KEPCO. First, we obtain refined data through data preprocessing and outlier removal. Next, our model is trained by extracting daily features per charging station and constructing a training set. Finally, our model is verified through performance analysis using a test set for each charging station type, and the limitations of our model are discussed.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.698-700
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• 1996

A knowledge based forecasting system for special days has been developed for the economic and secure operation of electric power system. If-then production rules has been adopted in this system to be used in various environmental conditions. Graphic user interfaces enables a user to access easily to the system. The simulation based on the historical data have shown that the forecasting result was improved remarkably when compared to the results from the conventional statistical methods. The forecasting results can be used for power system operational planning to improve security and economy of the power system.

• Proceedings of the KIEE Conference
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• 1998.11a
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• pp.292-294
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• 1998

It is very important for electric utility to expand generating facilities and transmission equipments in accordance with the increase of electricity demand. Regional electricity demand forecasting is among the most important step for long-term investment and power supply planning. The main objectives of this paper are to develop the methodologies for forecasting regional load demand. The Model consists of four models, regional economy, regional electricity energy demand, areal electricity energy demand. and areal peak load demand. This paper mainly suggests regional electricity energy demand model and areal peak load demand. A case study is also presented.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.177-181
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• 2009

Developing u-Public facilities for application u-City is to combine both the state-of-the art of the construction and ubiquitous computing and must be flexibly comprised of the facilities for the basic service of the building such as air conditioning, heating, lighting and electric equipments to materialize a new format of spatial planning and the public facilities inside or outside. Accordingly, in this paper we suggested the time pattern system for predicting the most basic power system loads for the basic service. To application the tim e pattern we applied SOM algorithm and k-means method and then clustered the data each weekday and each time respectively. The performance evaluation results of suggestion system showed that the forecasting system better the ARIMA model than the exponential smoothing method. It has been assumed that the plan for power supply depending on demand and system operation could be performed efficiently by means of using such power load forecasting.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.169-171
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• 1996

In this paper, we have developed bus load forecasting system (BUSLOF) based on Windows 95. It has been developed for the secure operation of electric power system. It forecasts regional load and bus load using regional distribution factor(RDF) and bus distribution factor (BDF) which are calculated from bus load in the past. It is equipped with graphic user interface(GUI) which enables a user to easily access to the system. The performance of the developed system is estimated in sample data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.2
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• pp.331-337
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• 2015

The power flow analysis of electrified railway is required complicated calculation, because of variable load. Train runs trough rail supplied by electric power therefore, the load value in electrified railway system fluctuates along time. The power flow algorithm in electrified railway system is different from general power system, and the power flow simulation is peformed by the particular simulation software. Powerail is simulation software for analysis of traction power supply system developed by KRRI, in 2008. This consists of load forecasting module, including TPS and time scheduling, and power flow module. This software was verified by measured current under normal feeding condition, however, has not been verified by voltage on the condition of extended feeding. This paper presents the verification of PowerRail based on voltage drop under extended feeding condition. This is performed by comparing simulation result with field test. Field test and simulation is done in commercial railway line.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1267-1269
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• 1999

This paper studies on arrangement of load correlation coefficient for advanced load management. To accurate load correlation coefficient, we used two real factors, electrical energy(kWh) and peak load current of pole transformers, acquired by measuring instrument. Out of several correlation equations, we find that the quadratic equation is the most accurate to express peak load current and working electrical energy. If the data is located in the outside of ${\pm}3{\sigma}$ it is discarded. For load management, we rearranged load correlation coefficient considering +2${\sigma}$ at load correlation equation. Comparing conventional load correlation coefficient with rearranged one, we can get the result of error reduced and it is adjacent to the actual data. It will be used peak load forecasting from working electrical energy and we are able to prevent from the damaging of pole transformer due to overload.

• Proceedings of the KIEE Conference
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• 2002.11b
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• pp.41-43
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• 2002

The contracted electric power and the demand factor of customers are used to predict the peak load in distribution transformers. The conventional demand factor was determined more than ten years ago. The contracted electric power and power demand have been increased. Therefore, we need to prepare the novel demand factor that appropriates at present. In this paper, we modify the demand factor to improve the peak load prediction of distribution transformers. To modify the demand factor, we utilize the 169 data acquisition devices for sample distribution transformers in winter, spring summer. And, the peak load currents were measured by the case studies using the actual load data, through which we verified that the proposed demand factors were correct than the conventional factors. A newly demand factor will be used to predict the peak load of distribution transformers.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1468-1471
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• 1999

This paper presents a method of the regional long-term distribution planning considering economic indicator with the assumption that energy demands proportionally increases with the economic indicators. For the practical distribution planning, it is necessary to regional load forecasting, distribution substation planning, distribution feeder planning. Accordingly, in this paper, after performing regional load forecasting considering economic indicator, it is performed distribution substation planning and distribution feeder planning in order by using this result. For accurate distribution planning, it is very important to scrutinize the correlation among the regional electric power demands, economic indicator and other characteristics because distribution planning results may vary depending on many different factors such as electric power demands, gross products, social trend and so on. In this paper, various steps microscopically and macro scopically are used for the regional long-term distribution planning in order to increase the accuracy and practical use of the results

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1248-1252
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• 1999

To provide electricity power of good quality, it is essential to establish generation of electricity plan in electric power system based on accurate power-demand prediction and cope with changes of power-need fluctuating constantly. The role of hydraulic-power generation of electricity in electric power system is of importance because responding to electric power-demand counts or reservoir-type hydraulic-power generation of electricity which is designed for additional load in electric power system. So hydraulic-power generation of electricity must have fast start reserve. But the amount of water, resources of reservoir-type hydraulic-power generation of electricity is restricted and multi-used, so the scheduling of management by exact forecasting the amount of water is critical. That is why efficient hydraulic-power generation of electricity makes a main role on pumping up the utility of energy and water resource. This thesis introduced the example of optimal generation of electricity plan establishment which is used in managing reservoir-type hydraulic-power generation of electricity.