• Journal of Korea Society of Industrial Information Systems
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• v.18 no.3
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• pp.35-46
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• 2013

In order to develop an accurate analytical model for domestic electricity demand forecasting, we propose a prediction method of the electric power demand pattern by combining SMO classification techniques and a dimension reduction conceptualized subspace clustering techniques suitable for high-dimensional data cluster analysis. In terms of electricity demand pattern prediction, hourly electricity load patterns and the demographic and geographic characteristics can be analyzed by integrating the wireless load monitoring data as well as sub-regional unit of census information. There are composed of a total of 18 characteristics clusters in the prediction result for the sub-regional demand pattern by using census information and power load of Seoul metropolitan area. The power demand pattern prediction accuracy was approximately 85%.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.307-314
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• 2022

Recently, researches using deep learning-based models are being actively conducted to replace statistical-based time series forecast techniques to predict electric power demand. The result of analyzing the researches shows that the performance of the LSTM-based prediction model is acceptable, but it is not sufficient for long-term regional-wide power demand prediction. In this paper, we propose a WaveNet deep learning model to predict electric power demand 24-hour-ahead with temperature data in order to achieve the prediction accuracy better than MAPE value of 2% which statistical-based time series forecast techniques can present. First of all, we illustrate a delated causal one-dimensional convolutional neural network architecture of WaveNet and the preprocessing mechanism of the input data of electric power demand and temperature. Second, we present the training process and walk forward validation with the modified WaveNet. The performance comparison results show that the prediction model with temperature data achieves MAPE value of 1.33%, which is better than MAPE Value (2.33%) of the same model without temperature data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.1-6
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• 2009

The traditional demand prediction was based on the technique wherein electric power corporations made monthly or seasonal estimation of electric power consumption for each area and subscription type for the next one or two years to consider both seasonally generated and local consumed amounts. Note, however, that techniques such as pricing, power generation plan, or sales strategy establishment were used by corporations without considering the production, comparison, and analysis techniques of the predicted consumption to enable efficient power consumption on the actual demand side. In this paper, to calculate the predicted value of electric power consumption on a short-term basis (15 minutes) according to the amount of electric power actually consumed for 15 minutes on the demand side, we performed comparison and analysis by applying a 15-minute interval prediction technique to the average and that to the time series analysis to show how they were made and what we obtained from the simulations.

• Journal of Advanced Navigation Technology
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• v.21 no.4
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• pp.365-370
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• 2017

This study analyze correlation between weekdays data and special days data of different power demand patterns, and builds a separate data set, and suggests ways to reduce power demand prediction error by using deep learning network suitable for each data set. In addition, we propose a method to improve the prediction rate by adding the environmental elements and the separating element to the meteorological element, which is a basic power demand prediction elements. The entire data predicted power demand using LSTM which is suitable for learning time series data, and the special day data predicted power demand using DNN. The experiment result show that the prediction rate is improved by adding prediction elements other than meteorological elements. The average RMSE of the entire dataset was 0.2597 for LSTM and 0.5474 for DNN, indicating that the LSTM showed a good prediction rate. The average RMSE of the special day data set was 0.2201 for DNN, indicating that the DNN had better prediction than LSTM. The MAPE of the LSTM of the whole data set was 2.74% and the MAPE of the special day was 3.07 %.

• Journal of Korean Society of Disaster and Security
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• v.14 no.2
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• pp.35-42
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• 2021

In order to produce electric vehicle demand forecasting information, which is an important element of the plan to expand charging facilities for electric vehicles, a model for predicting electric vehicle demand was proposed using Exponential Smoothing. In order to establish input data for the model, the monthly power demand of cities and counties was applied as independent variables, monthly electric vehicle charging stations, monthly electric vehicle charging stations, and monthly electric vehicle registration data. To verify the accuracy of the electric vehicle power demand prediction model, we compare the results of the statistical methods Exponential Smoothing (ETS) and ARIMA models with error rates of 12% and 21%, confirming that the ETS presented in this paper is 9% more accurate as electric vehicle power demand prediction models. It is expected that it will be used in terms of operation and management from planning to install charging stations for electric vehicles using this model in the future.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.3
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• pp.157-163
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• 2019

K-BEMS System was introduced to reduce peak load and to save total energy of the 120 buildings that KEPCO headquarter and branch offices use. K-BEMS system is composed of PV, battery, and hybrid PCS. In this system, ESS, PV, lighting is used to save building energy based on demand prediction. Currently, neural network technique for short past data is applied to demand prediction, and fixed scheduling method by operator for ESS charging/discharging is used. To enhance this system, KEPCO research institute has carried out this K-BEMS research project for 3 years since January 2016. As the result of this project, we developed new real-time highly reliable building demand prediction technique with error free and optimized automatic ESS charging/discharging technique. Through several field test, we can certify the developed algorithm performance successfully. So we will describe the details in this paper.

• Journal of IKEEE
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• v.23 no.1
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• pp.127-133
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• 2019

Electric power demand forecasting is one of the important research areas for future smart grid introduction. However, It is difficult to predict because it is affected by many external factors. Traditional methods of forecasting power demand have been limited in making accurate prediction because they use raw power data. In this paper, a probability-based CRBM is proposed to solve the problem of electric power demand prediction using raw power data. The stochastic model is suitable to capture the probabilistic characteristics of electric power data. In order to compare the mid-term power demand forecasting performance of the proposed model, we compared the performance with Recurrent Neural Network(RNN). Performance comparison using electric power data provided by the University of Massachusetts showed that the proposed algorithm results in better performance in mid-term energy demand forecasting.

• Proceedings of the KIEE Conference
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• 2005.07e
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• pp.31-33
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• 2005

The accelerating of industrial development causes electricity demand to increase. By that power equipments need high power, multi function and intelligence. Also consumers demand for guarantee power supplying of good quality and reasonable operating equipment. Also they require for reliance and stabilization of power facility. Therefore preventive maintenance of electric installation must be developed and improvement of domestic technical level is needed in the maintenance management of equipment. The diagnosis of trouble existence is technique that compares steady state with unusual condition, whereas the prediction technique makes a diagnosis of remaining equipments life. It is difficult for us to diagnose trouble existence of electric installation and to develop prediction method in building because of a wide scope for electric installation in building. And in this paper we will investigate diagnosis and prediction method for only switch part of electric installation in building.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.451-458
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• 2017

An Electric Vehicle (EV) is operated with the electric energy of a battery in place of conventional fossil fuels. Thus, a suitable charging infrastructure must be provided to expand the use of electric vehicles. Because the battery of an EV must be charged to operate the EV, expanding the number of EVs will have a significant influence on the power supply and demand. Therefore, to maintain the balance of power supply and demand, it is important to be able to predict the numbers of charging EVs and monitor the events that occur in the distribution system. In this paper, we predict the hourly charging rate of electric vehicles using transformation matrix, which can describe all behaviors such as resting, charging, and driving of the EVs. Simulation with transformation matrix in a specific region provides statistical results using the Monte-Carlo Method.

• Proceedings of the KIEE Conference
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• 2002.07a
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• pp.530-532
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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. 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.