• Nuclear Engineering and Technology
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• 제53권7호
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• pp.2377-2386
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• 2021

To model the atmospheric dispersion of radionuclides released from nuclear accident is very important for nuclear emergency. But the uncertainty of model parameters, such as source term and meteorological data, may significantly affect the prediction accuracy. Data assimilation (DA) is usually used to improve the model prediction with the measurements. The paper proposed a parameter bias transformation method combined with Lagrangian puff model to perform DA. The method uses the transformation of coordinates to approximate the effect of parameters bias. The uncertainty of four model parameters is considered in the paper: release rate, wind speed, wind direction and plume height. And particle swarm optimization is used for searching the optimal parameters. Twin experiment and Kincaid experiment are used to evaluate the performance of the proposed method. The results show that the proposed method can effectively increase the reliability of model prediction and estimate the parameters. It has the advantage of clear concept and simple calculation. It will be useful for improving the result of atmospheric dispersion model at the early stage of nuclear emergency.

• 한국정보전자통신기술학회논문지
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• 제14권4호
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• pp.307-313
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• 2021

소상공인 업체들의 매장은 다수의 전기기기를 사용하는 매장들이 대부분이며 특히 냉장 시스템을 이용한 매장이 많아 여름, 겨울의 계절 변화에 따라 전력의 수요가 변화하고 온도의 급변에 냉장 시스템을 적용시키지 못할 시에 많은 전력부하가 발생되어 심할 경우 전력공급의 차단이 발생됨에 따라 매장 내 자산에 손실을 미칠 수 있다. 이에 따라 본 논문에서는 매장의 에너지 수요율을 측정하고 에너지를 절감하기 위하여 LSTM 기반의 전력 부하 예측 시스템을 설계하였다. 이는 데이터 기반의 중소 매장용 전력절감 시스템으로 사용될 수 있어 향후 소상공인 데이터 기반의 전력 수요 예측 시스템으로 사용되고, 전력 부하로 인한 피해 방지 분야에서 사용될 것으로 예상된다.

• 대기
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• 제34권2호
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• pp.177-185
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• 2024

As increasing global interest in renewable energy due to the ongoing climate crisis, there is a growing need for efficient technologies to manage such resources. This study focuses on the predictive skill of daily solar power generation using weather observation and forecast data. Meteorological data from the Korea Meteorological Administration and solar power generation data from the Korea Power Exchange were utilized for the period from January 2017 to May 2023, considering both inland (Daejeon) and coastal (Busan) regions. Temperature, wind speed, relative humidity, and precipitation were selected as relevant meteorological variables for solar power prediction. All data was preprocessed by removing their systematic components to use only their residuals and the residual of solar data were further processed with weighted adjustments for homoscedasticity. Four models, MLR (Multiple Linear Regression), RF (Random Forest), DNN (Deep Neural Network), and RNN (Recurrent Neural Network), were employed for solar power prediction and their performances were evaluated based on predicted values utilizing observed meteorological data (used as a reference), 1-day-ahead forecast data (referred to as fore1), and 2-day-ahead forecast data (fore2). DNN-based prediction model exhibits superior performance in both regions, with RNN performing the least effectively. However, MLR and RF demonstrate competitive performance comparable to DNN. The disparities in the performance of the four different models are less pronounced than anticipated, underscoring the pivotal role of fitting models using residuals. This emphasizes that the utilized preprocessing approach, specifically leveraging residuals, is poised to play a crucial role in the future of solar power generation forecasting.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 춘계학술대회
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• pp.643-647
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• 2003

Power plant Piping operating at elevated temperature often fails prematurely by the growth of microcracks under creep conditions. Therefore, the life assessment of high temperature components that contain cracks is an important technological problem. The mechanisms of crack growth in simple metals and alloys have been investigated using both mechanical and microstructural approaches. In this study, life prediction accounting for creep, crack growth and thermal stress is analyzed.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2000년도 춘계학술대회논문집
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• pp.52-55
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• 2000

Investigated via a series of finite element process simulation is the effect of diverse process variables on some selected non-dimensional parameters characterizing the strip in hot strip rolling. Then on the basis of these parameters an on-line model is derived for the precise prediction of roll and roll power. The prediction accuracy of the proposed model is examined through comparison with predictions from a finite element process model.

• Journal of Power Electronics
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• 제11권1호
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• pp.59-63
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• 2011

This paper presents the modeling of the sampling effect in the p-type average current mode control. The prediction of the high frequency components near half of the switching frequency in the current loop gain is given for the p-type average current mode control. By the proposed model, the prediction accuracy is improved when compared to that of conventional models. The proposed method is applied to a buck converter, and then the measurement results are analyzed.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2011년도 춘계학술대회 논문집
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• pp.534-537
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• 2011

This Paper present prediction Vehicle Interior Noise using ATF(Acoustic Transfer Function) and engine radiated sound power. This is useful tool to qualifying the effectiveness of Air-borne noise Path. Furthermore This method provide acoustic package performance of the vehicle and able to prepare frequency band to same segment or benchmarking vehicle.

• 태양에너지
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• 제20권2호
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• pp.67-73
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• 2000

This study represents the methodology of performance prediction for small hydro power(SHP) sites. Nine tunnel type SHP sites with diversion dam were selected and the performance characteristics were analyzed by using a developed model. Also, primary design specifications such as design flowrate, plant capacity, and operational rate were suggested and feasibility for tunnel-type SHP sites were estimated. It was found that the design flowrate was most important parameter to exploit SHP plant and the methodology developed in this study was useful tool to analyze the performance of SHP sites.

• Journal of Electrical Engineering and Technology
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• 제10권1호
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• pp.41-46
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• 2015

It is very important to make accurate forecast of wind power because of its indispensable requirement for power system stable operation. The research is to predict wind power by chaos and BP artificial neural networks (CBPANNs) method based on genetic algorithm, and to evaluate feasibility of the method of predicting wind power. A description of the method is performed. Firstly, a calculation of the largest Lyapunov exponent of the time series of wind power and a judgment of whether wind power has chaotic behavior are made. Secondly, phase space of the time series is reconstructed. Finally, the prediction model is constructed based on the best embedding dimension and best delay time to approximate the uncertain function by which the wind power is forecasted. And then an optimization of the weights and thresholds of the model is conducted by genetic algorithm (GA). And a simulation of the method and an evaluation of its effectiveness are performed. The results show that the proposed method has more accuracy than that of BP artificial neural networks (BP-ANNs).

• Journal of Electrical Engineering and Technology
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• 제12권6호
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• pp.2177-2186
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• 2017

The goal of this paper is to provide the specific forecasting steps and to explain how to design the forecasting architecture and training data sets to forecast very short-term wind power when the numerical weather prediction (NWP) is unavailable, and when the sampling periods of the wind power and training data are different. We forecast the very short-term wind power every 15 minutes starting two hours after receiving the most recent measurements up to 40 hours for a total of 38 hours, without using the NWP data but using the historical weather data. Generally, the NWP works as a predictor and can be converted to wind power forecasts through machine learning-based forecasting algorithms. Without the NWP, we can still build the predictor by shifting the historical weather data and apply the machine learning-based algorithms to the shifted weather data. In this process, the sampling intervals of the weather and wind power data are unified. To verify our approaches, we participated in the 2017 wind power forecasting competition held by the European Energy Market conference and ranked sixth. We have shown that the wind power can be accurately forecasted through the data shifting although the NWP is unavailable.