• Journal of IKEEE
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• 제23권1호
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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.

• Aerospace Engineering and Technology
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• 제9권2호
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• pp.176-184
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• 2010

The power simulator for a LEO satellite is a useful tool to analyze mission validity and energy balance for various mission scenarios by estimating power generation, power consumption, depth of discharge, bus voltage, charging/discharging current, etc. In this paper, it is described the calculation algorithm of the solar array (SA) power, the satellite load power and the battery modeling method to develop a satellite power simulation. To simulate the SA power generation, three different operation modes (DET, MPPT, CV) of SAR (Solar Array Regulator) are considered with a SA model. The satellite load power is estimated using the satellite unit power database, the unit on/off configuration at some satellite operation modes. The bus voltage and battery charging/discharging current at the specific DoD (Depth of Discharge) are calculated based on the battery characteristics. By this satellite power simulator, it can be conveniently analyzed the energy balance and the validity of a planned mission of a LEO satellite.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제13권8호
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• pp.1647-1652
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• 2009

Generally the neural network and the fuzzy compensative algorithm are applied to forecast the time series for power demand with a characteristic of non-linear dynamic system, but it has a few prediction errors relatively. It also makes long term forecast difficult for sensitivity on the initial condition. On this paper, we evaluate the chaotic characteristic of electrical power demand with analysis methods of qualitative and quantitative and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction, time series forecast for multi dimension using Lyapunov exponent quantitatively. We compare simulated results with the previous method and verify that the purpose one being more practice and effective than it.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국해양정보통신학회 2009년도 춘계학술대회
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• pp.171-174
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• 2009

Generally the neural network and the fuzzy compensative algorithm are applied to forecast the time series for power demand with a characteristic of non-linear dynamic system, but it has a few prediction errors relatively. It also makes long term forecast difficult for sensitivity on the initial condition. On this paper, we evaluate the chaotic characteristic of electrical power demand with analysis methods of qualitative and quantitative and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction, time series forecast for multi dimension using Lyapunov exponent quantitatively. We compare simulated results with the previous method and verify that the purpose one being more practice and effective than it.

• The KIPS Transactions:PartA
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• 제9A권1호
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• pp.75-82
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• 2002

A robust signal transition density propagation method for a zero delay model is presented to obtain the signal transition density for estimating the power consumption. The power estimation for the zero delay model is a proper criteria for the lower boundary of power consumption. Since the input characteristics are generally unknown at design stage, robust estimation for wide range input characteristics is very important for the power consumption. In this paper, a conventional transition estimation method will be explored. And this exploration will be analyzed with the input/output signal transition behavior and used to propose the robust signal transition density propagation for the power estimation. In order to apply to practical circuits, the reconvergent path, which is crucial to affect the exactness of the power estimation, will be studied and an algorithm to take the reconvergent path into consideration will be presented. In experiment, the proposed methodology shows better robustness, comparable accuracy and elapsed time compared to the conventional methods.

• Journal of Korean Tunnelling and Underground Space Association
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• 제21권3호
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• pp.347-362
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• 2019

Underground excavation using TBM machines has been increasing to reduce complaints caused by noise, vibration, and traffic congestion resulted from the urban underground construction in Korea. However, TBM excavation design and construction still need improvement because those are based on standards of the technologically advanced countries (e.g., Japan, Germany) that do not consider geological environment in Korea at all. Above all, although TBM performance is a main factor determining the TBM machine type, duration and cost of the construction, it is estimated by only using UCS (uniaxial compressive strength) as the ground parameters and it often does not match the actual field conditions. This study was carried out as part of efforts to predict penetration rate suitable for Korean ground conditions. The effective parameters were defined through the correlation analysis between the penetration rate and the geotechnical parameters or TBM performance parameters. The effective parameters were then used as variables of the multiple regression analysis to derive a regression model for predicting TBM penetration rate. As a result, the regression model was estimated by UCS and joint spacing and showed a good agreement with field penetration rate measured during TBM excavation. However, when this model was applied to another site in Korea, the prediction accuracy was slightly reduced. Therefore, in order to overcome the limitation of the regression model, further studies are required to obtain a generalized prediction model which is not restricted by the field conditions.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• 제17권6호
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• pp.54-59
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• 2003

This paper presents hourly system marginal price forecasting of the Korea electric power system using a fuzzy linear regression analysis method. The proposed method is tested by forecasting hourly system marginal price for a week of spring in 2002. The percent average of forecasting error for the proposed method is from 3.14% to 6.10% in the weekdays, from 7.04% to 8.22% in the weekends, and comparable with a artificial neural networks method.

• Journal of Advanced Navigation Technology
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• 제21권4호
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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 %.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2006년도 추계학술대회
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• pp.273-276
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• 2006

신재생에너지의 효율 향상을 위한 시스템 개발과 병행하여 검토되어야 할 부문이 경제성 분석이다. 본 논문은 제주도 전력계통에 연계하여 운영중인 풍력발전의 자원과 발전량을 모의하기 위하여 제주도의 전력수요와 발전설비 특성, HDVC 수전 데이터, 풍속 등의 자료를 기반으로 가격예측을 위한 범용 소프트웨어들을 사용하여 에너지 시장 측면에서 풍력발전이 갖는 경제적 가치를 분석하였다.

• Electronics and Telecommunications Trends
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• 제18권2호통권80호
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• pp.45-52
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• 2003

전력선에 의한 통신선에 대한 유도장애 대책을 위하여 장애를 일으킬 수 있는 유도전압 제한치 초과 여부를 예측하여야 하는 바 전력선과 통신선이 유도 관계에 놓이게 되는 인접 상태는 두 가지 거리 요소에 의하여 결정된다. 즉 이격거리와 병행거리에 의하여 기본 유도 예측 규격이 결정되는 것으로서 본 논문에서는 실질적인 상기 두 가지 요소의 유도 발생 근접 범위를 예측 계산 사례를 통하여 그 범위 규격을 설정하도록 유도기관과 피유도기관 양자의 계산 자료를 활용 국내 기술기준 고시상에 반영되도록 연구분석한내용을 소개한다.