• 산업기술연구
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• 제28권B호
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• pp.199-207
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• 2008

A linear wind prediction program, WAsP, was employed to predict wind speed at two different sites located in complex terrain in South Korea. The reference data obtained at locations more than 7 kilometers away from the prediction sites were used for prediction. The predictions from the linear model were compared with the measured data at the two prediction sites. Two compensation methods such as a self-prediction error method and a delta ruggedness index (RIX) method were used to improve the wind speed prediction from WAsP and showed a good possibility. The wind speed prediction errors reached within 3.5 % with the self prediction error method, and within 10% with the delta RIX method. The self prediction error method can be used as a compensation method to reduce the wind speed prediction error in WAsP.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2008년도 추계학술대회 논문집
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• pp.268-273
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• 2008

A linear wind prediction program, WAsP, was employed to predict wind speed at two different sites located in complex terrain in South Korea. The reference data obtained at locations more than 7 kilometers away from the prediction sites were used for prediction. The predictions from the linear model were compared with the measured data at the two prediction sites. Two compensation methods such as a self-prediction error method and a delta ruggedness index (RIX) method were used to improve the wind speed prediction from WAsP and showed a good possibility. The wind speed prediction errors reached within 3.5 % with the self prediction error method, and within 10% with the delta RIX method. The self prediction error method can be used as a compensation method to reduce the wind speed prediction error in WAsP.

• 전기학회논문지
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• 제65권5호
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• pp.851-856
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• 2016

Wind speed is heavily fluctuated and quite local than other weather elements. It is difficult to improve the accuracy of prediction only in a numerical prediction model. An MOS (Model Output Statistics) technique is used to correct the systematic errors of the model using a statistical data analysis. The Most of previous MOS has used a linear regression model for weather prediction, but it is hard to manage an irregular nature of prediction of wind speed. In order to solve the problem, a nonlinear regression method using SVR (Support Vector Regression) is introduced for a development of MOS for wind speed prediction. Experiments are performed for KLAPS (Korea Local Analysis and Prediction System) re-analysis data from 2007 to 2013 year for Jeju Island and Busan area in South Korea. The MLR and SVR based linear and nonlinear methods are compared to each other for prediction accuracy of wind speed. Also, the comparison experiments are executed for the variation in the number of UM elements.

• 한국항공운항학회지
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• 제27권4호
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• pp.27-36
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• 2019

An accurate trajectory prediction is a key to the safe and efficient operations of aircraft. One way to improve trajectory prediction accuracy is to develop a model for aircraft ground speed prediction. This paper proposes a generative model for posterior aircraft ground speed prediction. The proposed method fits the Gaussian Mixture Model(GMM) to historical data of aircraft speed, and then the model is used to generates probabilistic speed profile of the aircraft. The performances of the proposed method are demonstrated with real traffic data in Incheon Flight Information Region(FIR).

• 한국콘텐츠학회논문지
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• 제20권4호
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• pp.25-37
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• 2020

교통 혼잡으로 인한 사회적 비용이 증가하면서 도로 속도를 예측하기 위한 다양한 연구들이 진행되고 있다. 도로 속도 예측의 정확도를 향상시키기 위해서는 교통 돌발 상황을 고려할 필요가 있다. 본 논문에서는 교통 돌발 상황을 고려한 도로 속도 예측 기법을 제안한다. 제안하는 기법은 연결된 도로들이 미치는 영향을 반영하기 위해서 예측 도로의 속도 데이터 뿐만 아니라 연결된 도로들의 속도 데이터도 이용한다. 또한, 돌발 상황으로 인한 혼잡을 예측하기 위해 속도의 변화량을 분석한다. 연결된 도로와 타겟 도로의 속도 데이터를 LSTM의 입력 데이터로 이용하여 1차적으로 도로 속도를 예측한다. 교통 돌발 상황으로 도로의 규칙적인 흐름이 깨지며 발생하는 예측 오차를 줄이기 위해 이벤트 가중치를 적용하여 최종적으로 도로 속도를 예측한다. 다양한 성능 평가를 통해 제안된 방법의 우수성을 입증한다.

• Journal of Electrical Engineering and Technology
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• 제8권5호
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• pp.1040-1048
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• 2013

In this paper, a sensorless pitch angle control method for a wind generation system is suggested. One-step-ahead prediction control law is adopted to control the pitch angle of a wind turbine in order for electric output power to track target values. And it is shown that this control scheme using the inverse dynamics of the controlled system enables us to predict current wind speed without an anemometer, to a considerable precision. The inverse input-output of the controlled system is realized by use of an artificial neural network. The proposed control and wind speed prediction method is applied to a Double-Feed Induction Generation system connected to a simple power system through computer simulation to show its effectiveness. The simulation results demonstrate that the suggested method shows better control performances with less control efforts than a conventional Proportional-Integral controller.

• 풍력에너지저널
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• 제15권2호
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• pp.10-22
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• 2024

This research is a comprehensive analysis of wind power prediction sensitivity using a Long Short-Term Memory (LSTM) deep learning neural network model, accounting for the inherent uncertainties in wind speed estimation. Utilizing a year's worth of operational data from an operational wind farm, the study forecasts the power output of both individual wind turbines and the farm collectively. Predictions were made daily at intervals of 10 minutes and 1 hour over a span of three months. The model's forecast accuracy was evaluated by comparing the root mean square error (RMSE), normalized RMSE (NRMSE), and correlation coefficients with actual power output data. Moreover, the research investigated how inaccuracies in wind speed inputs affect the power prediction sensitivity of the model. By simulating wind speed errors within a normal distribution range of 1% to 15%, the study analyzed their influence on the accuracy of power predictions. This investigation provided insights into the required wind speed prediction error rate to achieve an 8% power prediction error threshold, meeting the incentive standards for forecasting systems in renewable energy generation.

• 전기학회논문지
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• 제66권12호
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• pp.1799-1803
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• 2017

This paper introduces the prediction methods of windstorm advisory using GP nonlinear compensation and SVM. The existing special report prediction is not specialized for strong wind, such as windstorm, because it is based on the wide range of predicted values for wind speed from low to high. In order to improve the performance of strong wind reporting prediction, a method that can efficiently classify boundaries of strong wind is necessary. First, evolutionary nonlinear regression based compensation technique is applied to obtain more accurate values of prediction for wind speed using UM data. Based on the prediction wind speed, the windstorm advisory is determined. Second, SVM method is applied to classify directly using the data of UM predictors and windstorm advisory. Above two methods are compared to evaluate of the performances for the windstorm data in Jeju Island in South Korea. The data of 2007-2009, 2011 year is used for training, and 2012 year is used for test.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2011년도 추계학술대회 논문집
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• pp.843-848
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• 2011

Planning and construction of railway for high speed trains up to 400 km/h are recently driven in Korea. High speed train is one of the environment-friendly fastest mass transportation means but its noise generated by rolling, traction and aerodynamic mechanism can cause public complaints of residents nearby railways. To cost-effectively prevent the troublesome noise in a railway planning stage, the rational railway noise prediction method considering the characteristics of trains as well as railway structures should be required but it is difficult to find authentic methods for Korean high speed trains such as KTX and KTX-II. In this study, we propose a framework of our own railway noise prediction model emitted by Korean high speed trains over 250 km/h based on the recent research results carried out in EU countries. The model considers railway sound power level using several point sources distributed in heights as well as tracks, whose detail speed- and frequency-dependent emission characteristics of Korean high speed trains should be determined in near future by measurement or numerical analysis. The attenuation during propagation outdoors is calculated by the well-known ISO 9613-2 and auxiliary methods to consider undulated terrain and wind effect.

• 대한토목학회논문집
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• 제31권1D호
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• pp.43-50
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• 2011

택시 프로브(Probe)를 이용한 구간통행속도 모니터링체계는 지능형교통체계(ITS)의 핵심적인 하부시스템 중 하나이다. 택시 프로브기법을 통해 수집되는 구간통행속도는 도시가로망의 교통상태 모니터링과 통행시간 정보제공에 널리 활용되고 있다. 그러나 택시 Probe기법은 표본수가 적고 교통혼잡으로 인하여 구간통행시간이 자료수집 주기보다 큰 경우, 실시간으로 자료가 수집되지 않는 누락상태가 발생하게 된다. 이러한 누락상태는 단일시간대에서 다중시간대에 걸쳐 발생하게 되며, 기존의 단일시간대 예측기법으로는 다중시간대의 상태를 예측하지 못하는 단점이 있다. 따라서 다중시간대 누락상태에서 실시간 구간통행속도를 예측하기위한 기법이 요구된다. 본 연구에서는 기존의 단일시간대 예측기법의 한계를 극복하면서 단일 및 다중시간대 통행속도를 예측하기위한 기법을 개발하였다. 개발된 모형은 비모수회귀(NPR)을 기반으로 개발되었으며, 다중시간대 예측에도 불구하고 기존의 단일시간대 예측기법보다 우수한 정확도를 보였다.