• 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.

• Journal of Digital Convergence
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• v.12 no.3
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• pp.211-218
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• 2014

Recently, wind energy is expanding to combination of computing to forecast of wind power generation as well as intelligent of wind powerturbine. Wind power is rise and fall depending on weather conditions and difficult to predict the output for efficient power production. Wind power is need to reliably linked technology in order to efficient power generation. In this paper, distributed power generation forecasts to enhance the predicted and actual power generation in order to minimize the difference between the power of distributed power short-term prediction model is designed. The proposed model for prediction of short-term combining the physical models and statistical models were produced in a physical model of the predicted value predicted by the lattice points within the branch prediction to extract the value of a physical model by applying the estimated value of a statistical model for estimating power generation final gas phase produces a predicted value. Also, the proposed model in real-time National Weather Service forecast for medium-term and real-time observations used as input data to perform the short-term prediction models.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.39-44
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• 2023

Renewable energy such as solar and wind power is a resource that is sensitive to weather conditions and environmental changes. Since the amount of power generated by a facility can vary depending on the installation location and structure, it is important to accurately predict the amount of power generation. Using meteorological data, a data preprocessing process based on principal component analysis was conducted to monitor the relationship between features that affect energy production prediction. In addition, in this study, the prediction was tested by reconstructing the dataset according to the sensitivity and applying it to the machine learning model. Using the proposed model, the performance of energy production prediction using random forest regression was confirmed by predicting energy production according to the meteorological environment for new and renewable energy, and comparing it with the actual production value at that time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.942-945
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• 2010

The development of decentralized power has appeared as part of an effort to decrease the energy loss and the cost for electric power facilities through installing small renewable energy generation systems including solar and wind power generation. Recently a new era for decentralized power environment in building is coming in order to handle the climatic and environmental change occurred all over the world. Especially solar and wind power generation systems can be easily set up and are also economically feasible, and thus many industrial companies enter into this business. This paper suggests the overall architecture for the decentralized renewable power system and the prediction method of power on climatic change. The ultimate goal is to help manage the overall power efficiently and thus provide the technological basis for achieving zero-energy house.

• 한국신재생에너지학회:학술대회논문집
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• 2009.11a
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• pp.409-412
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• 2009

In this research, the intensity of solar energy, which was injected to the different angle plane every hour day by day, was technically documented and quantitatively analyzed through actual observations. In order to group every days into days with similar intensity, graph was drawn with respect to time for every day and each area value under the curve was calculated. Then, the search for grouped days having similar intensity curve patterns was carried out and the optimum incident angle of absorber plate was derived to maximize the efficiency of solar energy systems.

• Journal of the Korean Solar Energy Society
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• v.31 no.5
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• pp.27-32
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• 2011

Most of domestic building generally don't have fixed shading devices considering of appearance and aesthetic issues. In this study is suggested that tilt window simultaneously has a role of shading and blocking solar radiation. The tilt window thermal performance is investigated by relation ship between inclination and heating cooling road. As comparing vertical window with $5^{\circ}$ and $7^{\circ}$ of tilt window respectively, the heating load is increased by 3.6% and cooling load is reduced by 8.1% on $5^{\circ}$ tilt window and the heating load is increased by 5.3% and cooling load is reduced by 11.5% on $5^{\circ}$ tilt window. Especially, the total load of alternative tilt window is showed the reduction rate 2.6% and3.6% compared of vertical window. Therefore, the tilt window is possible to role of shading of solar radiation and reduction of heating and cooling load.

• Solar Energy
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• v.18 no.4
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• pp.67-75
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• 1998

Daylight illuminance are always changing. Nevertheless, when the energy savings due to daylight are calculated an accurate estimate of daylight availability is required. Where artificial lighting is photoelectrically controlled the relevant quantity is the cumulative distribution of daylight illuminance. This paper describes an experiment which measured daylight illuminance over one whole working year. Also using measured data on availability of daylight, equations are drived to predict the maximum possible savings from photoelectric controls for an interior lighting installation. The equations are applied to a space as a worked example and figures are given for the relative maximum savings in artificial lighting use of three control systems: on/off, dimming and mixed.

• Journal of the Korean Solar Energy Society
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• v.23 no.2
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• pp.1-7
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• 2003

Due to the lack of fossil fuel the demand for the development of alternative energy is gradually growing. There are solar energy and underground energy as the alternative energies for housing. To use underground energy, we need some data on the underground temperature but the data are very rare in our country. So we need tools to calculate the underground temperature. In this paper a method to calculate the underground temperature is sought with the latitude, the level, and the distance from sea for the district without the measured data.

• Journal of the Korean Solar Energy Society
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• v.39 no.2
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• pp.71-80
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• 2019

Daily ahead forecast is necessary for the electricity balance between load and supply due to the variability renewable energy. Numerical weather prediction is usually employed to produce the solar irradiance as well as electric power forecast for more than 12 hours forecast horizon. UM-LDAPS model is the numerical weather prediction operated by Korea Meteorological Administration and it generates the 36 hours forecast of hourly total irradiance 4 times a day. This study attempts to evaluate the model performance against the in situ measurements at 37 ground stations from January to May, 2013. Relative mean bias error, mean absolute error and root mean square error of hourly total irradiance are averaged over all ground stations as being 8.2%, 21.2% and 29.6%, respectively. The behavior of mean bias error appears to be different; positively largest in Chupoongnyeong station but negatively largest in Daegu station. The distinct contrast might be attributed to the limitation of microphysics parameterization for thick and thin clouds in the model.

• Information and Communications Magazine
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• v.15 no.9
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• pp.97-106
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• 1998

It is crucial to predict the variabilities of the near-earth space environment associated with the solar activity, which cause enormous socio-economic impacts on mankind. The geomagnetic storm prediction scheme adopted in this study is designed to predict such variabilities in terms of the geomagnetic indices, AE and Dst, the cross-polar cap potential difference, the energy dissipation rate over the polar ionosphere and associated temperature increase in the thermosphere. The prediction code consists of two parts; prediction of the solar wind and interplanetary magnetic field based upon actual flare observations and estimation of various electrodynamic quantities mentioned above from the solar wind-magnetosphere coupling function 'epsilon' which is derivable through the predicted solar wind parameters. As a test run, the magnetic storm that occurred in early November, 1993, is simulated and the results are compared with the solar wind and the interplanetary magnetic field measured by the Japanese satellite, Geotail, and the geomagnetic indices obtained from ground magnetic observatories. Although numerous aspects of the code are to be further improved, the comparison between the simulated results and the actual measurements encourages us to use this prediction scheme as the first appoximation in forecasting the disturbances of the near-earth space environment associated with solar flares.