• Journal of Institute of Control, Robotics and Systems
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• v.20 no.1
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• pp.22-28
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• 2014

This paper proposes the fuzzy modeling and robust stability analysis of wind farm based on prediction model for wind speed. Owing to the sensitivity of wind speed, it is necessary to study the dynamic equation of the variable speed wind turbine. In this paper, based on the least-square method, the wind speed prediction model which is varied by the surrounding environment is proposed so that it is possible to evaluate the practicability of our model. And, we propose the composition of intelligent wind farm and use the fuzzy model which is suitable for the design of fuzzy controller. Finally, simulation results for wind farm which is modeled mathematically are demonstrated to visualize the feasibility of the proposed method.

• Wind and Structures
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• v.39 no.2
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• pp.85-100
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• 2024

In light of extreme value distribution probability, an improved prediction method of the Recurrence Period Wind Speed (RPWS) is constructed considering wind direction, with the Equivalent Independent Wind Direction Number (EIWDN) introduced as a parameter variable. Firstly, taking the RPWS prediction of Beijing city as an example, the traditional Cook method is used to predict the RPWS of each wind direction based on the measured wind speed data in Beijing area. On basis of the results, the empirical formulae to determine the parameter variables are fitted to construct an improved expression of the non-exceedance probability of the RPWS. In this process, the statistical model of the optimal threshold is established, and thus the independent wind speed samples exceeding the threshold are extracted and fitted to follow the Generalized Pareto Distribution (GPD) model for analysis. In addition, the Extreme Value Type I (EVT I) distribution model is used to predict and analyze the RPWS. To verify its wide applicability, the improved method is further used in cities like Jinan, Nanjing, Wuxi, Shanghai and Shenzhen to predict and analyze the RPWS of each wind direction, and the prediction results are compared against those gained via the traditional Cook method and the whole direction. Results show that the 50-year RPWS results predicted by the improved method are basically consistent with those predicted by the traditional method, and the RPWS prediction values of most wind directions are within the envelope range of the whole wind direction prediction value. Compared with the traditional method, the improved method can readily predict the RPWS under different return periods through empirical formulae, and avoid the repeated operation process and some assumptions in the traditional Cook method, and then improve the efficiency of prediction. In addition, the improved RPWS prediction results corresponding to the GPD model are slightly larger than those of the EVT I distribution model.

• Journal of the Korea Society of Computer and Information
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• v.20 no.3
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• pp.19-27
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• 2015

Technologies of wind power generation for development of alternative energy technology have been accumulated over the past 20 years. Wind power generation is environmentally friendly and economical because it uses the wind blowing in nature as energy resource. In order to operate wind power generation efficiently, it is necessary to accurately predict wind speed changing every moment in nature. It is important not only averagely how well to predict wind speed but also to minimize the largest absolute error between real value and prediction value of wind speed. In terms of generation operating plan, minimizing the largest absolute error plays an important role for building flexible generation operating plan because the difference between predicting power and real power causes economic loss. In this paper, we propose a method of wind speed prediction using numeric prediction algorithm-based wind speed forecast model made to analyze the wind speed forecast given by the Meteorological Administration and pattern value for considering seasonal property of wind speed as well as changing trend of past wind speed. The wind speed forecast given by the Meteorological Administration is the forecast in respect to comparatively wide area including wind generation farm. But it contributes considerably to make accuracy of wind speed prediction high. Also, the experimental results demonstrate that as the rate of wind is analyzed in more detail, the greater accuracy will be obtained.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.1
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• pp.1-16
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• 2020

With the advent of the fourth industrial revolution era, there has been a growing interest in deep learning using big data, and studies using deep learning have been actively conducted in various fields. In the transportation sector, there are many advantages to using deep learning in research as much as using deep traffic big data. In this study, a short -term travel speed prediction model using LSTM, a deep learning technique, was constructed to predict the travel speed. The LSTM model suitable for time series prediction was selected considering that the travel speed data, which is used for prediction, is time series data. In order to predict the travel speed more precisely, we constructed a model that reflects both temporal and spatial effects. The model is a short-term prediction model that predicts after one hour. For the analysis data, the 5minute travel speed collected from the Seoul Transportation Information Center was used, and the analysis section was selected as a part of Gangnam where traffic was congested.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.107-112
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• 2015

This paper introduces an evolutionary nonlinear regression based compensation technique for the short-range prediction of wind speed using AWS(Automatic Weather Station) data. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, but a linear regression based MOS is hard to manage an irregular nature of weather prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP(Genetic Programming) is suggested for a development of MOS wind forecast guidance. Also FCM(Fuzzy C-Means) clustering is adopted to mitigate bias of wind speed data. The purpose of this study is to evaluate the accuracy of the estimation by a GP based nonlinear MOS for 3 days prediction of wind speed in South Korean regions. This method is then compared to the UM model and has shown superior results. Data for 2007-2009, 2011 is used for training, and 2012 is used for testing.

• New & Renewable Energy
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• v.18 no.1
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• pp.17-28
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• 2022

This paper presents an effective Markov transition matrix (EMTM), which will be used to calculate the wind speed at the target site in a wind farm to accurately predict wind energy production. The existing MTS prediction method using a Markov transition matrix (MTM) exhibits a limitation where significant prediction variations are observed owing to random selection errors and its bin width. The proposed method selects the effective states of the MTM and refines its bin width to reduce the error of random selection during a gap filling procedure in MTS. The EMTM reduces the level of variation in the repeated prediction of wind speed by using the coefficient of variations and range of variations. In a case study, MTS exhibited better performance than other MCP models when EMTM was applied to estimate a one-day wind speed, by using mean relative and root mean square errors.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.477-482
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• 2015

Linear regressions and evolutionary nonlinear regression based compensation techniques for the short-range prediction of wind speed are investigated. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, but a linear regression based MOS is hard to manage an irregular nature of weather prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP(Genetic Programming) is suggested for a development of MOS for wind speed prediction. The proposed method is compared to various linear regression methods for prediction of wind speed. Also, statistical analysis of distribution for UM elements for each method is executed. 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.

• Journal of Internet Computing and Services
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• v.11 no.3
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• pp.25-32
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• 2010

It takes quite amount of time to study a game because there are many game characters and different stages are exist for games. This paper used reinforcement learning algorithm for characters to learn, and so they can move intelligently. On learning early, the learning speed becomes slow. Improved sequential prediction method was used to improve the speed of learning. To compare a normal learning to an improved one, a game was created. As a result, improved character‘s ability was improved 30% on learning speed.

• Wind and Structures
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• v.18 no.6
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• pp.619-631
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• 2014

This paper proposes new criteria to fix hidden neuron in Multilayer Perceptron Networks for wind speed prediction in renewable energy systems. To fix hidden neurons, 101 various criteria are examined based on the estimated mean squared error. The results show that proposed approach performs better in terms of testing mean squared errors. The convergence analysis is performed for the various proposed criteria. Mean squared error is used as an indicator for fixing neuron in hidden layer. The proposed criteria find solution to fix hidden neuron in neural networks. This approach is effective, accurate with minimal error than other approaches. The significance of increasing the number of hidden neurons in multilayer perceptron network is also analyzed using these criteria. To verify the effectiveness of the proposed method, simulations were conducted on real time wind data. Simulations infer that with minimum mean squared error the proposed approach can be used for wind speed prediction in renewable energy systems.

• Journal of Korean Society of Transportation
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• v.28 no.5
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• pp.141-153
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• 2010

The study is about the development of operating speed prediction models aimed for an evaluation of design consistency of four lane rural roads. The main differences of this study relative to previous research are the method of data collection and classification of road alignments. The previous studies collected speed data at several points in the horizontal curve and approaching tangent. This method of collection is based on the assumption that acceleration and deceleration only occurs at horizontal tangents and the speed is kept constant at horizontal curves. However, this assumption leads to an unreliable speed estimation, so drivers' behavior is not well represented. Contrary to the previous approach, speed data were collected with one and data analysis using a speed profile is made for data selection before building final models. A total of six speed prediction models were made according to the combination of horizontal and vertical alignments. The study predicts that the speed data analysis and selection for model building employed in this study can improve the prediction accuracy of models and be useful to analyze drivers' speed behavior in a more detailed way. Furthermore, it is expected that the operating speed prediction models can help complement the current design-speed-based guidelines, so more benefits to drivers as real road users, rather than engineers or decision makers, can be achieved.