• Journal of Advanced Navigation Technology
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• v.21 no.6
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• pp.643-650
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• 2017

Photovoltaic generation which has unlimited energy sources are very intermittent because they depend on the weather. Therefore, it is necessary to get accurate generation prediction with reducing the uncertainty of photovoltaic generation and improvement of the economics. The Meteorological Agency predicts weather factors for three days, but doesn't predict the sunshine and solar radiation that are most correlated with the prediction of photovoltaic generation. In this study, we predict sunshine and solar radiation using weather, precipitation, wind direction, wind speed, humidity, and cloudiness which is forecasted for three days at Meteorological Agency. The photovoltaic generation forecasting model is proposed by using predicted solar radiation and sunshine. As a result, the proposed model showed better results in the error rate indexes such as MAE, RMSE, and MAPE than the model that predicts photovoltaic generation without radiation and sunshine. In addition, DNN showed a lower error rate index than using SVM, which is a type of machine learning.

• 한국신재생에너지학회:학술대회논문집
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• 2011.11a
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• pp.131-131
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• 2011

태양광 및 태양열 설비의 효율적인 관리를 위해서는 관련 일사정보가 사전정보로 제공되어 시스템 운용을 위한 입력인자로 활용되어야 한다. 특히 전력그리드에 연계되어 설비가 활용된다고 하면, 그 에너지 공급이 불규칙적인 신재생에너지원의 특성으로 인해 에너지 공급량의 예측이 선행되어 기존의 전력공급체계가 이를 지원할 수 있는 모델과 시스템이 구비되어야 한다. 기존의 다양한 연구들이 한정된 국소지점에 대해 다양한 예측기법을 적용하여 평가를 실시하였지만, 장기간의 결과 축적이 이루어지지 못해 그 신뢰성 확보에 어려움을 겪고 있다. 본 연구에서는 현재 한국에너지기술연구원에서 관리되는 일사정보를 활용하여 청명한 날의 표준 일사 데이터베이스를 생성하고, 기상청에서 RSS(Rich Site Summary) 형태로 지원하는 운량정보를 이용하여 3시간 이상의 미래정보를 계속적으로 산출할 수 있는 시스템을 제작하고자 하였다.

• 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.

• Journal of Institute of Convergence Technology
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• v.9 no.1
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• pp.31-36
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• 2019

After 30 years of KKP model analysis and extended 30 years of accuracy analysis, the unique correlation and various problems between measured horizontal surface insolation and measured weather data are found in this paper. The KKP model's 10yrs daily total horizontal surface insolation forecasting was averaged about 97.7% on average, and the forecasting accuracy at peak times per day was about 92.1%, which is highly applicable regardless of location and weather conditions nationwide. The daily total solar radiation forecasting accuracy of the modified KKP cloud model was 98.9%, similar to the KKP model, and 93.0% of the forecasting accuracy at the peak time per day. And the results of evaluating the accuracy of calculation for 30 years of KKP model were cloud model 107.6% and cloud model 95.1%. During the accuracy analysis evaluation, this study found that inaccuracies in measurement data of cloud cover should be clearly assessed by the Meteorological Administration.

• 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.

• 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.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.196-200
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• 2024

Weather forecasting is considered to be of utmost important among various important sectors such as flood management and hydro-electricity generation. Although there are various numerical methods for weather forecasting but majority of them are reported to be Mechanistic computationally demanding due to their complexities. Therefore, it is necessary to develop and build models for accurately predicting the weather conditions which are faster as well as efficient in comparison to the prevalent meteorological models. The study has been undertaken to forecast various atmospheric parameters in the city of Bangalore using Naïve Bayes algorithms. The individual parameters analyzed in the study consisted of wind speed (WS), wind direction (WD), relative humidity (RH), solar radiation (SR), black carbon (BC), radiative forcing (RF), air temperature (AT), bar pressure (BP), PM₁₀ and PM_2.5 of the Bangalore city collected from Air Quality Monitoring Station for a period of 5 years from January 2015 to May 2019. The study concluded that Naive Bayes is an easy and efficient classifier that is centered on Bayes theorem, is quite efficient in forecasting the various air pollution parameters of the city of Bangalore.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.1
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• pp.70.1-70.1
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• 2014

We study the origin and acceleration mechanism of solar energetic particles (SEPs), which are one of the major causes of hazardous impacts in the space weather. By adopting the velocity dispersion to the multi-channel energy band observations from SOHO/ERNE and Wind/3DP, we estimate the onset time for each energy band and investigate coronal structure and CME's dynamics associated with the SEPs. Through this study we will find clues to answer the questions about the origin and acceleration of SEPs as well as their associated with flare and/or CMEs. We will apply our findings to improve the forecasting system of the solar radiation storms.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1414-1424
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• 2021

In this paper, the importance of input factors of a DNN (Deep Neural Network) PM_2.5 forecasting model using LRP(Layer-wise Relevance Propagation) is analyzed, and forecasting performance is improved. Input factor importance analysis is performed by dividing the learning data into time and PM_2.5 concentration. As a result, in the low concentration patterns, the importance of weather factors such as temperature, atmospheric pressure, and solar radiation is high, and in the high concentration patterns, the importance of air quality factors such as PM_2.5, CO, and NO₂ is high. As a result of analysis by time, the importance of the measurement factors is high in the case of the forecast for the day, and the importance of the forecast factors increases in the forecast for tomorrow and the day after tomorrow. In addition, date, temperature, humidity, and atmospheric pressure all show high importance regardless of time and concentration. Based on the importance of these factors, the LRP_DNN prediction model is developed. As a result, the ACC(accuracy) and POD(probability of detection) are improved by up to 5%, and the FAR(false alarm rate) is improved by up to 9% compared to the previous DNN model.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.49-62
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• 2023

Solar energy, which is rapidly increasing in proportion, is being continuously developed and invested. As the installation of new and renewable energy policy green new deal and home solar panels increases, the supply of solar energy in Korea is gradually expanding, and research on accurate demand prediction of power generation is actively underway. In addition, the importance of solar radiation prediction was identified in that solar radiation prediction is acting as a factor that most influences power generation demand prediction. In addition, this study can confirm the biggest difference in that it attempted to predict solar radiation using medium-term forecast weather data not used in previous studies. In this paper, we combined the multi-linear regression model, KNN, random fores, and SVR model and the clustering technique, K-means, to predict solar radiation by hour, by calculating the probability density function for each cluster. Before using medium-term forecast data, mean absolute error (MAE) and root mean squared error (RMSE) were used as indicators to compare model prediction results. The data were converted into daily data according to the medium-term forecast data format from March 1, 2017 to February 28, 2022. As a result of comparing the predictive performance of the model, the method showed the best performance by predicting daily solar radiation with random forest, classifying dates with similar climate factors, and calculating the probability density function of solar radiation by cluster. In addition, when the prediction results were checked after fitting the model to the medium-term forecast data using this methodology, it was confirmed that the prediction error increased by date. This seems to be due to a prediction error in the mid-term forecast weather data. In future studies, among the weather factors that can be used in the mid-term forecast data, studies that add exogenous variables such as precipitation or apply time series clustering techniques should be conducted.