• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1342-1348
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• 2015

The objective of this study is to propose a method to calculate prediction intervals for one-day-ahead hourly forecasts of photovoltaic power generation and to evaluate its performance. One year of data of two systems, representing contrasting examples of forecast’ accuracy, were used. The method is based on the maximum likelihood estimation, the similarity between the input data of future and past forecasts of photovoltaic power, and on an assumption about the distribution of the error of the forecasts. Two assumptions for the forecast error distribution were evaluated, a Laplacian and a Gaussian distribution assumption. The results show that the proposed method models well the photovoltaic power forecast error when the Laplacian distribution is used. For both systems and intervals calculated with 4 confidence levels, the intervals contained the true photovoltaic power generation in the amount near to the expected one.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.3
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• pp.393-398
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• 2015

City or Community-scale Energy Management System(CEMS) is used to reduce the total energy consumed in the city by arranging the energy resources efficiently at the planning stage and controlling them economically at the operating stage. Of the operational functions of the CEMS, generation forecasting of renewable energy resources is an essential feature for the effective supply scheduling. This is because it can develop daily operating schedules of controllable generators in the city (e.g. diesel turbine, micro-gas turbine, ESS, CHP and so on) in order to minimize the inflow of the external power supply system, considering the amount of power generated by the uncontrollable renewable energy resources. This paper is written to introduce numerical models for photo-voltaic power generation prediction based on the weather forecasting information. Unlike the conventional methods using the average radiation or average utilization rate, the proposed models are developed for CEMS applications using the realtime weather forecast information provided by the National Weather Service.

• Transactions of the Korean hydrogen and new energy society
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• v.31 no.5
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• pp.436-443
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• 2020

The performance prediction model of a solid oxide fuel cell stack has been developed using deep neural network technique, one of the machine learning methods. The machine learning has been received much interest in various fields, including energy system mo- deling. Using machine learning technique can save time and cost requried in developing an energy system model being compared to the conventional method, that is a combination of a mathematical modeling and an experimental validation. Results reveal that the mean average percent error, root mean square error, and coefficient of determination (R2) range 1.7515, 0.1342, 0.8597, repectively, in maximum. To improve the predictability of the model, the pre-processing is effective and interpolative machine learning and application is more accurate than the extrapolative cases.

• KEPCO Journal on Electric Power and Energy
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• v.1 no.1
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• pp.163-168
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• 2015

In order to integrate high wind generation resources into power grid, it is an essential to predict power outputs of wind generating resources. As wind farm outputs depend on natural wind resources that vary over space and time, spatial modeling based on geographic information such as latitude and longitude is needed to estimate power outputs of wind generation resources. In this paper, we introduce the basic concept of spatial modeling and present the spatial prediction model based on Kriging techniques. The empirical data, wind farm power output in Texas, is considered to verify the proposed prediction model.

• Korean Journal of Computational Design and Engineering
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• v.21 no.2
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• pp.143-150
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• 2016

In shipbuilding, hull assemblies are manufactured by welding. The thermal deformation caused by the welding produces shape deformation. Counter-deformed design methods have been used in shipyards to cope with the weld-induced deformation of ship assembles. Finite element methods (FEMs) are frequently used to estimate welding distortion in the counter-deformed design. For the estimation of welding distortion, producing uniform rectangular elements is required to enter thermal loads on the welding line and obtain accurate analysis results. In this paper, a new automatic mesh generation method is proposed for prediction of welding deformation in FEM. Meshes are constructed for test cases to demonstrate the feasibility of the proposed mesh generation method.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.4 s.97
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• pp.229-236
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• 1999

The surface, generated by end milling operation, is deteriorated by tool runout, vibration, tool wear and tool deflection, etc. Among them, the effect of tool deflection on surface accuracy is analyzed. Surface generation model for the prediction of the topography of machined srufaces has been developed based on cutting mechanism and cutting tool geometry. This model accounts for not only the ideal geometrical surface, but also the deflection of tool due to cutting force. For the more accurate prediction of cutting force, flexible end mill model is used to simulate cutting process. Computer simulation has shown the feasibility of the surface generation system. Using developed simulation system, the relations between the shape of end mill and cutting conditions are analyzed.

• Journal of IKEEE
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• v.26 no.3
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• pp.355-363
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• 2022

Korea is pursuing a plan to switch and expand energy sources with a focus on renewable energy with the goal of becoming carbon neutral by 2050. As the instability of energy supply increases due to the intermittent nature of renewable energy, accurate prediction of the amount of renewable energy generation is becoming more important. Therefore, the government has opened a small-scale power brokerage market and is implementing a system that pays settlements according to the accuracy of renewable energy prediction. In this paper, a prediction model was implemented using a statistical model and an artificial intelligence model for the prediction of solar power generation. In addition, the results of prediction accuracy were compared and analyzed, and the revenue from the settlement amount of the renewable energy generation forecasting system was estimated.

• Journal of the Korea Society for Simulation
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• v.17 no.3
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• pp.19-26
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• 2008

In this paper, we propose a dynamic prediction algorithm to predict the bond price using actual data set of treasure note (T-Note). The proposed algorithm is based on term structure model of the interest rates, which takes place in various financial modelling, such as the standard Gaussian Wiener process. To obtain cumulative distribution functions (CDFs) of actual data for the interest rate measurement used, we use the natural cubic spline (NCS) method, which is generally used as numerical methods for interpolation. Then we also use the random number generation scheme (RNGS) to calculate the pricing of bond through the obtained CDF. In empirical computer simulations, we show that the lower values of precision in the proposed prediction algorithm corresponds to sharper estimates. It is very reasonable on prediction.

• Journal of IKEEE
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• v.25 no.4
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• pp.766-769
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• 2021

In order to perform tasks such as design, control, optimization, and prediction of flight vehicle trajectories based on machine learning techniques including deep learning, a certain amount of flight vehicle trajectory data is required. However, there are cases in which it is difficult to secure more than a certain amount of flight vehicle trajectory data for various reasons. In such cases, synthetic data generation could be one way to make machine learning possible. In this paper, to explore this possibility, we generated and evaluated synthetic flight vehicle trajectory data using time-series generative adversarial neural network. In addition, various ablation studies (comparative experiments) were performed to explore the possibility of using synthetic data in the aircraft trajectory prediction task. The experimental results presented in this paper are expected to be of practical help to researchers who want to conduct research on the possibility of using synthetic data in the generation of synthetic flight vehicle trajectory data and the work related to flight vehicle trajectories.

• Asia pacific journal of information systems
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• v.30 no.1
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• pp.31-52
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• 2020

Stock markets are popular investment avenues to people who plan to receive premium returns compared to other financial instruments, but they are highly volatile and risky due to the complex financial dynamics and poor understanding of the market forces involved in the price determination. A system that can forecast, predict the stock prices and automatically create a portfolio of top performing stocks is of great value to individual investors who do not have sufficient knowledge to understand the complex dynamics involved in evaluating and predicting stock prices. In this paper the authors propose a Stock prediction, Portfolio Generation and Selection model based on Machine learning algorithms, Artificial neural networks (ANNs) are used for stock price prediction, Mathematical and Statistical techniques are used for Portfolio generation and Un-Supervised Machine learning based on K-Means Clustering algorithms are used for Portfolio Evaluation and Selection which take in to account the Portfolio Return and Risk in to consideration. The model presented here is limited to predicting stock prices on a long term basis as the inputs to the model are based on fundamental attributes and intrinsic value of the stock. The results of this study are quite encouraging as the stock prediction models are able predict stock prices at least a financial quarter in advance with an accuracy of around 90 percent and the portfolio selection classifiers are giving returns in excess of average market returns.