• Atmosphere
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• v.34 no.2
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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.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.77-87
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• 2021

In this study, a novel improved second order Radial Basis Function Neural Network based method with excellent scheduling capabilities is used for the dynamic prediction of short and long-term energy required applications. The effectiveness and the reliability of the algorithm are evaluated using training operations with New England-ISO database. The dynamic prediction algorithm is implemented in Matlab and the computation of mean absolute error and mean absolute percent error, and training time for the forecasted load, are determined. The results show the impact of temperature and other input parameters on the accuracy of solar Photovoltaic load forecasting. The mean absolute percent error is found to be between 1% to 3% and the training time is evaluated from 3s to 10s. The results are also compared with the previous studies, which show that this new method predicts short and long-term load better than sigmoidal neural network and bagged regression trees. The forecasted energy is found to be the nearest to the correct values as given by England ISO database, which shows that the method can be used reliably for short and long-term load forecasting of any electrical 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.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.42.1-42.1
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• 2021

In this study, we present a visual explanation of a deep learning solar flare forecast model and its relationship to physical parameters of solar active regions (ARs). For this, we use full-disk magnetograms at 00:00 UT from the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, physical parameters from the Space-weather HMI Active Region Patch (SHARP), and Geostationary Operational Environmental Satellite X-ray flare data. Our deep learning flare forecast model based on the Convolutional Neural Network (CNN) predicts "Yes" or "No" for the daily occurrence of C-, M-, and X-class flares. We interpret the model using two CNN attribution methods (guided backpropagation and Gradient-weighted Class Activation Mapping [Grad-CAM]) that provide quantitative information on explaining the model. We find that our deep learning flare forecasting model is intimately related to AR physical properties that have also been distinguished in previous studies as holding significant predictive ability. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of daily solar flare occurrence with TSS = 0.65, without any preprocessing to extract features from data. Second, using the attribution methods, we find that the polarity inversion line is an important feature for the deep learning flare forecasting model. Third, the ARs with high Grad-CAM values produce more flares than those with low Grad-CAM values. Fourth, nine SHARP parameters such as total unsigned vertical current, total unsigned current helicity, total unsigned flux, and total photospheric magnetic free energy density are well correlated with Grad-CAM values.

• 한국신재생에너지학회:학술대회논문집
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• 2008.10a
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• pp.174-177
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• 2008

The amount of incident solar rays on inclined surfaces with various directions has Since the atmospheric clearness index is main factor for evaluating atmosphere environment, it is necessary to estimate its characteristics all over the major cities in Korea Peninsula. We have begun collecting clearness index data since 1982 at 16 different cities in South Korea and estimated using empirical forecasting models at 12 different stations over the North Korea from 1982 to 2006. This considerable effort has been made for constructing a standard value from measured data at each city. The new clearness data for global-dimming analysis will be extensively used by evaluating atmospheric environment as well as by solar application system designer or users.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.54 no.4
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• pp.188-194
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• 2005

There are many factors that affect on the system output of Photovoltaic(PV) power generation; the variation of solar radiation, temperature, energy conversion efficiency of solar cell etc. This paper suggests a methodology for calculation of PV generation output using the probability distribution function of irradiance, PV array efficiency and revision factors of solar cell conversion efficiency. Long-term irradiance data recorded every hour of the day for 11 years were used. For goodness-fit test, several distribution (unctions are tested by Kolmogorov-Smirnov(K-S) method. The calculated generation output with or without revision factors of conversion efficiency is compared with that of CMS (Centered Monitoring System), which can monitor PV generation output of each PV generation site.

• Korean System Dynamics Review
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• v.7 no.2
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• pp.35-56
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• 2006

Korea is implementing strong regulatory derives such as Feed in Tariff to provide incentives for renewable energy developers. But if the government is planning to increase the renewable capacity with only "Price policy" not considering the investors behavior in the competitive electricity market, the policy would be failed. It is necessary system thinking and simulation model analysis to decide government's incentive goal. This study is focusing on the assesment of the competitiveness of renewable energy with the current Feed in Tariff incentives compared to the traditional energy source, specially coal and gas. The simulation results show that the market penetration of renewable energy with the current Feed-in-Tariff level is about 60-70% of the government goal under condition that the solar energy and fuel cell are assumed to provide the whole capacity set in the governmental goal. If the contribution from solar and fuel cell is lower than planned, the total penetration of renewable energy will be dropped more. Notably, Wind power turned out to be proved only 10% of government goal because of its low availability.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.1
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• pp.36.1-36.1
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• 2011

It is generally believed that eruptive phenomena in the solar atmosphere such as solar flares and coronal mass ejections (CMEs) occur in the solar active regions with complex magnetic structures. Magnetic helicity has been recognized as a useful parameter to measure the complexity such as twists, kinks, and inter-linkages of magnetic field lines. The objective of this study is to understand a long-term (a few days) variation of magnetic helicity in active regions and its relationship with the energy buildup and instability leading to flares and CMEs. Statistical studies of flare productivity and magnetic helicity injection in about 400 active regions were carried out. The temporal variation of magnetic helicity injected through the photosphere of active regions was also examined related to 46 CMEs. The main findings in this study are as follows: (1) the study of magnetic helicity for active regions producing major flares and CMEs indicates that there is always a significant helicity injection through the active-region photosphere over a long period of 0.5 - a few days before the flares and CMEs; (2) for the 30 CMEs under investigation, it is found that there is a fairly good correlation (linear correlation coefficient of 0.71) between the average helicity injection in the CME-productive active regions and the CME speed. Beside the scientific contribution, a major impact of this study is the observational discovery of a characteristic variation pattern of magnetic helicity injection in flare/CME-productive active regions which can be used for the improvement of solar eruption forecasting.

• 한국태양에너지학회:학술대회논문집
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• 2009.11a
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• pp.68-73
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• 2009

Since the atmospheric clearness index is main factor for evaluating atmosphere environment, it is necessary to estimate its characteristics all over the major cities in Korea Peninsula. We have begun collecting clearness index data since 1982 at 16 different cities in South Korea and estimated using empirical forecasting models at 21 different stations over the North Korea from 1982 to 2008. This considerable effort has been made for constructing a standard value from measured data at each city. The new clearness data for global-dimming analysis will be extensively used by evaluating atmospheric environment as well as by solar PV application system designer or users. From the results, we can conclude that. 1) Yearly mean 63.5 % of the atmospheric clearness index was evaluated for clear day all over the 37 cities in Korea Peninsula, 2) Clear day's atmospheric clearness index of spring and summer were 64.6% and 64.8%, and for fall and winter their values were 63.3% and 61.3% respectively in Korea Peninsula.

• 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.