• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1874-1885
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• 2018

The customer side operation is getting more complex in a smart grid environment because of the adoption of renewable resources. In performing energy management planning or scheduling, it is essential to forecast non-controllable resources accurately and robustly. The PV system is one of the common renewable energy resources in customer side. Its output depends on weather and physical characteristics of the PV system. Thus, weather information is essential to predict the amount of PV system output. However, weather forecast usually does not include enough solar irradiation information. In this study, a PV system power output prediction model (PPM) under limited weather information is proposed. In the proposed model, meteorological radiation model (MRM) is used to improve cloud cover radiation model (CRM) to consider the seasonal effect of the target region. The results of the proposed model are compared to the result of the conventional CRM prediction method on the PV generation obtained from a field test site. With the PPM, root mean square error (RMSE), and mean absolute error (MAE) are improved by 23.43% and 33.76%, respectively, compared to CRM for all days; while in clear days, they are improved by 53.36% and 62.90%, respectively.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.535-538
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• 2001

Since 1990s, many literatures have shown that connectionist models, such as back propagation, recurrent network, and RBF (Radial Basis Function) outperform the traditional models, MA (Moving Average), AR (Auto Regressive), and ARIMA (Auto Regressive Integrated Moving Average) in time series prediction. Neural based approaches to time series prediction require the enough length of historical measurements to generate the enough number of training patterns. The more training patterns, the better the generalization of MLP is. The researches about the schemes of generating artificial training patterns and adding to the original ones have been progressed and gave me the motivation of developing VTG schemes in 1996. Virtual term is an estimated measurement, X(t+0.5) between X(t) and X(t+1), while the given measurements in the series are called actual terms. VTG (Virtual Tern Generation) is the process of estimating of X(t+0.5), and VTG schemes are the techniques for the estimation of virtual terms. In this paper, the alternative VTG schemes to the VTG schemes proposed in 1996 will be proposed and applied to multivariate time series prediction. The VTG schemes proposed in 1996 are called deterministic VTG schemes, while the alternative ones are called stochastic VTG schemes in this paper.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.1126-1135
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• 2022

The MARS code has been assessed for the prediction of onset of flow instability (OFI) in a vertical channel. For assessment, we built an experiment database that consists of experiments under various geometry and thermal-hydraulic condition. It covers pressure from 0.12 to 1.73 MPa; heat flux from 0.67 to 3.48 MW/m²; inlet sub-cooling from 39 to 166 ℃; hydraulic diameters between 2.37 and 6.45 mm of rectangular channels and pipes. It was shown that the MARS code can predict the OFI mass flux for pipes reasonably well. However, it could not predict the OFI in a rectangular channel well with a mean absolute percentage error of 8.77%. In the cases of rectangular channels, the error tends to depend on the hydraulic diameter. Because the OFI is directly related to the subcooled boiling in a flow channel, we suggest a modified subcooled boiling model for better prediction of OFI in a rectangular channel; the net vapor generation (NVG) model and the modified wall evaporation model were modified so that the effect of hydraulic diameter and heat flux can be accurately considered. The assessment of the modified model shows the prediction of OFI mass flux for rectangular channels is greatly improved.

• The korean journal of orthodontics
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• v.52 no.2
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• pp.102-111
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• 2022

Objective: To develop and explore the usefulness of an artificial intelligence system for the prediction of the need for dental extractions during orthodontic treatments based on gender, model variables, and cephalometric records. Methods: The gender, model variables, and radiographic records of 214 patients were obtained from an anonymized data bank containing 314 cases treated by two experienced orthodontists. The data were processed using an automated machine learning software (Auto-WEKA) and used to predict the need for extractions. Results: By generating and comparing several prediction models, an accuracy of 93.9% was achieved for determining whether extraction is required or not based on the model and radiographic data. When only model variables were used, an accuracy of 87.4% was attained, whereas a 72.7% accuracy was achieved if only cephalometric information was used. Conclusions: The use of an automated machine learning system allows the generation of orthodontic extraction prediction models. The accuracy of the optimal extraction prediction models increases with the combination of model and cephalometric data for the analytical process.

• Smart Media Journal
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• v.13 no.2
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• pp.9-15
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• 2024

In this paper, a Diffusion Multi-step Classifier (DMC) is proposed to address the imbalance issue in credit prediction. DMC utilizes a Diffusion Model to generate continuous numerical data from credit prediction data and creates categorical data through a Multi-step Classifier. Compared to other algorithms generating synthetic data, DMC produces data with a distribution more similar to real data. Using DMC, data that closely resemble actual data can be generated, outperforming other algorithms for data generation. When experiments were conducted using the generated data, the probability of predicting delinquencies increased by over 20%, and overall predictive accuracy improved by approximately 4%. These research findings are anticipated to significantly contribute to reducing delinquency rates and increasing profits when applied in actual financial institutions.

• KIISE Transactions on Computing Practices
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• v.23 no.11
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• pp.632-640
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• 2017

Although solar insolation is the weather factor with the greatest influence on power generation in photovoltaic systems, the Meterological Agency does not provide solar insolation data for future dates. Therefore, it is essential to research prediction methods for solar insolation to efficiently manage photovoltaic systems. In this study, we propose a Dynamic Piecewise Prediction Model that can be used to predict solar insolation values for future dates based on information from the weather forecast. To improve the predictive accuracy, we dynamically divide the entire data set based on the sun altitude and cloudiness at the time of prediction. The Dynamic Piecewise Prediction Model is developed by applying a polynomial linear regression algorithm on the divided data set. To verify the performance of our proposed model, we compared our model to previous approaches. The result of the comparison shows that the proposed model is superior to previous approaches in that it produces a lower prediction error.

• Journal of Convergence Society for SMB
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• v.6 no.4
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• pp.93-98
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• 2016

Recently, the project related to the smart grid are being actively studied around the developed world. In particular, the long-term stabilization measures distributed power supply problem has been highlighted. In this paper, we propose a three-dimensional numerical weather prediction models to compare the error rate information which combined with the physical models and statistical models to predict the output of distributed power. Proposed model can predict the system for a stable power grid-can improve the prediction information of the distributed power. In performance evaluation, proposed model was a generation forecasting accuracy improved by 4.6%, temperature compensated prediction accuracy was improved by 3.5%. Finally, the solar radiation correction accuracy is improved by 1.1%.

• The Journal of the Acoustical Society of Korea
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• v.39 no.2
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• pp.77-86
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• 2020

In this study, the Eulerian/Lagrangian one-way coupling method is proposed to predict flow noise due to Blade-Tip Vortex Cavitation (BTVC). The proposed method consists of four sequential steps: flow field simulation using Computational Fluid Dynamics (CFD) techniques, reconstruction of wing-tip vortex using vortex model, generation of BTVC using bubble dynamics model and acoustic wave prediction using the acoustic analogy. Because the CFD prediction of tip vortex structure generally suffers from severe under-prediction of its strength along the steamwise direction due to the intrinsic numerical damping of CFD schemes and excessive turbulence intensity, the wing-tip vortex along the freestream direction is regenerated by using the vortex modeling. Then, the bubble dynamics model based on the Rayleigh-Plesset equation was employed to simulate the generation and variation of BTVC. Finally, the flow noise due to BTVC is predicted by modeling each of spherical bubbles as a monople source whose strength is proportional to the rate of time-variation of bubble volume. The validity of the proposed numerical methods is confirmed by comparing the predicted results with the measured data.

• Journal of Navigation and Port Research
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• v.27 no.2
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• pp.111-119
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• 2003

In order to improve navigational safety of ships, an ocean wave prediction model of high precision within a short time, dealing with multi-directional random waves from the information of the sea surface winds encountered at the planned ship's course, was introduced for construction of ocean wave forecasting system on the ship. In this paper, we investigated a sea disaster occurred by a stormy weather in the past. We analyzed the sea surface wind first and then carried out ocean wave hindercasting simulations according to the routes the sunken vessel. From the result of this study, we concluded that the sea disaster was caused by rapidly developed iou pressure system Okhotsk Sea and the predicted values by the third generation wave prediction model(WAM) was agreed well with the observed significant wave height, wave period, and directional wave spectrum. It gives a good applicability for construction of a practical on-board calculation system.

• Journal of the Korean Solar Energy Society
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• v.30 no.6
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• pp.66-72
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• 2010

In this study, we analyzed the characteristics of wind speed and wind direction at different locations in Jeju area using past 10 years observed data and used them in our wind power forecasting model. Generally the strongest hourly wind speeds were observed during daytime(13KST~15KST) whilst the strongest monthly wind speeds were measured during January and February. The analysis with regards to the available wind speeds for power generation gave percentages of 83%, 67%, 65% and 59% of wind speeds over 4m/s for the locations Gosan, Sungsan, Jeju site and Seogwipo site, respectively. Consequently the most favorable periods for power generation in Jeju area are in the winter season and generally during daytime. The predicted wind speed from the forecast model was in average lower(0.7m/s) than the observed wind speed and the correlation coefficient was decreasing with longer prediction times(0.84 for 1h, 0.77 for 12h, 0.72 for 24h and 0.67 for 48h). For the 12hour prediction horizon prediction errors were about 22~23%, increased gradually up to 25~29% for 48 hours predictions.