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

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.10
• /
• pp.1423-1427
• /
• 2014

Quantity of the solar power generation is heavily influenced by weather. In other words, due to difference in insolation, different quantity may be generated. However, it does not mean all areas with identical insolation produces same quantity because of various environmental aspects. Additionally, geographic factors such as altitude, height of plant may have an impact on the quantity. Hence, through this research, we designed a system to predict efficiency of the solar power generation system by applying insolation, weather factor such as duration of sunshine, cloudiness parameter and location. By applying insolation, weather data that are collected from various places, we established a system that fits with our nation. Apart from, we produced a geographic model equation through utilizing generated data installed nationwide. To design a prediction model that integrates two factors, we apply fuzzy algorithm, and validate the performance of system by establishing simulation system.

• Journal of the Korean Solar Energy Society
• /
• v.36 no.1
• /
• pp.1-9
• /
• 2016

To well design the solar energy system, the correlation to calculate and predict solar irradiation is basically needed. So, this study was performed to reveal the relationships between the solar irradiation and four meteorological observation data(dry-bulb temperature, relative humidity, duration of sunshine, and amount of cloud) that didn't show from previous any other researches. And then, we finally proposed the various order non-linear correlation from the measured solar irradiation and four meteorological measurement data using MINITAB. To show the deviation and accuracy of the solar irradiation between measured and calculated, this study compared for the daily total solar insolation. From those results, the calculation error could well predicted about maximum 97% for the daily total solar insolation. But, the coefficients of the proposed correlations didn't show any relationships. So, needs more studies to make the proper one correlation for the country.

• Journal of the Korean Solar Energy Society
• /
• v.37 no.1
• /
• pp.25-38
• /
• 2017

Climate change and environmental pollution are becoming serious due to the use of fossil energy. For this reason, renewable energy systems are increasing, especially photovoltaic systems being more popular. The photovoltaic system has characteristics that are affected by ambient weather conditions such as insolation, outside temperature, wind speed. Particularly, it has been confirmed that the performance of the photovoltaic system decreases as the module temperature increases. In order to grasp the influence of the module temperature in advance, several researchers have proposed the prediction models on the module temperature. In this paper, we predicted the module temperature using the aforementioned prediction model on the basis of the weather conditions in Incheon, South Korea during July and August. The influence of weather conditions (i.e. insolation, outside temperature, and wind speed) on the accuracy of the prediction models was also evaluated using the standard statistical metrics such as RMSE, MAD, and MAPE. The results show that the prediction accuracy is reduced by 3.9 times and 1.9 times as the insolation and outside temperature increased respectively. On the other hand, the accuracy increased by 6.3 times as the wind speed increased.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.37 no.12
• /
• pp.1105-1112
• /
• 2013

In this study, a method for predicting heating loads using building characteristic coefficients is proposed for heating system control, and a method for predicting hourly temperature and solar insolation, which mainly affect building heating loads, is also proposed. The temperature and solar insolation are predicted by using a fuzzy theory from forecast information at the meteorological agency, and the building characteristic coefficients for the prediction of heating loads are derived from EnergyPlus. The simulated heating loads of the present study show good agreement with those of EnergyPlus. and the variations of the predicted heating loads using the predicted temperature and solar insolation are similar to those using the actual weather data.

• Journal of Internet of Things and Convergence
• /
• v.8 no.3
• /
• pp.55-61
• /
• 2022

In order to reduce greenhouse gases, the main culprit of global warming, the United Nations signed the Climate Change Convention in 1992. Korea is also pursuing a policy to expand the supply of renewable energy to reduce greenhouse gas emissions. The expansion of renewable energy development using solar power led to the expansion of wind power and solar power generation. The expansion of renewable energy development, which is greatly affected by weather conditions, is creating difficulties in managing the supply and demand of the power system. To solve this problem, the power brokerage market was introduced. Therefore, in order to participate in the power brokerage market, it is necessary to predict the amount of power generation. In this paper, the prediction system was used to analyze the Yonchuk solar power plant. As a result of applying solar insolation from on-site (Model 1) and the Korea Meteorological Administration (Model 2), it was confirmed that accuracy of Model 2 was 3% higher. As a result of comparative analysis of the DNN and RNN models, it was confirmed that the prediction accuracy of the DNN model improved by 1.72%.

• Journal of Korea Multimedia Society
• /
• v.19 no.8
• /
• pp.1530-1537
• /
• 2016

Recently, solar power generation shows the significant growth in the renewable energy field. Using the short-term prediction, it is possible to control the electric power demand and the power generation plan of the auxiliary device. However, a short-term prediction can be used when you know the weather forecast. If it is not possible to use the weather forecast information because of disconnection of network at the island and the mountains or for security reasons, the accuracy of prediction is not good. Therefore, in this paper, we proposed a system capable of short-term prediction of solar power generation amount by using only the weather information that has been collected by oneself. We used temperature, humidity and insolation as weather information. We have applied a moving average to each information because they had a characteristic of time series. It was composed of min, max and average of each information, differences of mutual information and gradient of it. An artificial neural network, SVM and RBF Network model was used for the prediction algorithm and they were combined by Ensemble method. The results of this suggest that using a moving average during pre-processing and ensemble prediction models will maximize prediction accuracy.

• Journal of Environmental Impact Assessment
• /
• v.5 no.1
• /
• pp.95-105
• /
• 1996

Meteorological Joint Frequency Function required indispensably in long-term air quality prediction models were discussed for practical application in Korea. The algorithm, proposed by Turner(l964), is processed with daily solar insolation and cloudiness and height basically using Pasquill's atmospheric stability classification method. In spite of its necessity and applicability, the computer program, called STAR(STability ARray), had some significant difficulties caused from the difference in meteorological data format between that of original U.S. version and Korean's. To cope with the problems, revised STAR program for Korean users were composed of followings; applicability in any site of Korea with regard to local solar angle modification; feasibility with both of data which observed by two classes of weather service centers; and examination on output format associated with prediction models which should be used.

• Journal of the Korean Solar Energy Society
• /
• v.38 no.6
• /
• pp.27-36
• /
• 2018

Typical Meteorological Year Dataset is necessary for the renewable energy feasibility study. Since National Renewable Energy Laboratory has been built Typical Meteorological Year Dataset in 1978, gridded datasets taken from numerical weather prediction or satellite imagery are employed to produce Typical Meteorological Year Dataset. In general, Typical Meteorological Year Dataset is generated by using long-term in-situ observations. However, solar insolation is not usually measured at synoptic observing stations and therefore it is limited to build the Typical Meteorological Year Dataset with only in-situ observation. This study attempts to build the Typical Meteorological Year Dataset with satellite derived solar insolation as an alternative and then we evaluate the Typical Meteorological Year Dataset made by using satellite derived solar irradiance at Daejeon ground station. The solar irradiance is underestimated when satellite imagery is employed.

• Journal of Institute of Convergence Technology
• /
• v.4 no.2
• /
• pp.27-33
• /
• 2014

To well design the solar energy system using solar energy, the correlation to calculate solar irradiation is basically needed. So, this study was performed to reveal the relationships between the solar irradiation and four meteorological observation data(dry bulb temperature, relative humidity, sunshine duration, and cloud cover) which are different from previous other researches. And then, we finally proposed the first order non-linear correlation from the measured solar irradiation using four meteorological observation data with MINITAB. To show the deviation of the solar irradiation between measured and calculated, this study compared using the daily total solar irradiance and the maximum peak value. From those results, the calculation error was estimated about maximum 25.4% for the daily total solar irradiance. The error of the solar irradiation between measured and calculated was made from the curve fitting error. So, solar irradiation prediction correlation with higher accuracy can be obtained using 2nd or higher order terms with four meteorological observation data.