• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.10
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• pp.1423-1427
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• 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 Korea Multimedia Society
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• v.19 no.8
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• pp.1530-1537
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• 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 the Korean Institute of Electrical and Electronic Material Engineers
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• v.30 no.11
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• pp.699-704
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• 2017

The amount of electric power for photovoltaic power generation depends on the location of the power plant and the direction of solar cell. The solar cell controls the generation of solar power plants. Therefore, the structure of solar cell, manufacturing method, and optic technology were factors contributing to increased solar cell efficiency; however, the technical limit has been reached. Herein, we propose a new method to increase the solar cell efficiency using a wavelength conversion technology that converts ultraviolet and infrared rays, which are not effectively used in solar cells, into effective wavelength of solar cell. We used fluoride $Na(Ca)YF_4$ phosphor for wavelength conversion. Then, a wavelength-conversion fluorescent paste, prepared using an organic-silicon binder, was used to prepare a film that was applied to Si solar cells. It was confirmed that conversion efficiency improved by 5% or more.

• Current Photovoltaic Research
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• v.12 no.1
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• pp.17-23
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• 2024

Photovoltaic (PV) power generation is the world's best and largest renewable energy that generates electricity with infinite sunlight. Solar cell modules are a component of photovoltaic power generation and must have a long-term durability of at least 25 years. The development of processes and equipment that can be recovered through the recycling of metals and valuable metals when the solar module's lifespan is over has been completed to the level of commercialization, but few processes have been developed that require repair due to initial defects. This is mainly due to the economic problems caused by remaking. However, if manufacturing processes such as repairing solar cell modules that have been proven to be early defects are established and the technical review of long-term reliability and durability reaches a certain level, it is considered that it will be a recommended process technology for environmental economics. In this paper, assuming that a defective solar cell module occurs artificially, a manufacturing process for replacement of solar cells was developed, and a technical verification of the manufacturing technology was conducted through long-term durability evaluation in accordance with KS C 8561. Through this, it was determined that remanufacturing technology for solar cell replacement of solar cell modules that occurred in a short period of time after installation was possible, and the research results were announced through a journal to commercialize solar modules using manufacturing technology in the solar market in the future.

• Design & Manufacturing
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• v.15 no.3
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• pp.50-55
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• 2021

Solar power generation, which is one of the methods of using solar energy, has a high possibility of practical implementation compared to other renewable energy power generation, and it has the characteristic that it can generate as much power as needed in necessary places. In addition, maintenance is easy, unmanned operation is possible, and power management can be performed more efficiently if operated in a hybrid method with existing electric energy. Therefore, in this study, numerical analysis using a computer program was performed to analyze the efficient operation and performance improvement of solar energy of the rotating condensing type solar LED street lamp. As a result, the two-axis tracking type could obtain 15.23 % more electricity per year than the fixed type, and additional auxiliary power generation was required for the fixed type by 19 % per year than the tracking type. As a result of computational fluid dynamics(CFD) simulation for PV module surface temperature prediction, the The surface temperature of the Photovoltaics(PV) module incident surface was predicted to be about 10℃ higher than that of the fixed type.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.54-56
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• 2005

The paper proposes the solar photovoltaic power generation system method for photovoltaic system to solve the power shortage due the sudden power demand. So that supplied electric power to system at appearance during surplus electric power minute and unit moment link driving with common use system is available, digital PLL circuit system voltage through composition and phase of solar photovoltatic power generation system to do synchronization do. Feed forward controller was applied to get fast current response Solar cell that is changed by solar radiation always kept the maximum output when it used Step up chopper. The dynamic character had checked through simulation used Matlab Sumulink and confirmed through an experiment.

• Proceedings of the KIPE Conference
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• 2008.10a
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• pp.190-192
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• 2008

The government plans to deploy 100,000 photovoltaic system in residential houses by 2012. In this paper, I described how to design the Residence PV System and how to simulated which predict the efficiency of its electrical generation. AS comparing the simulated data and actual installed 3kW photovoltaic power generation system. I analyze the condition of the Residence PV system and suggest the best way to design in best condition.

• Land and Housing Review
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• v.11 no.1
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• pp.87-94
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• 2020

To implement the planning of zero-energy buildings, their energy performance must be improved, and renewable energy applications must also be included. To accelerate the use of renewable energies in such buildings, BIPVs should be actively used in windows and on roofs. Window-type BIPVs are being developed in various forms depending on the size, composition, area ratio of the window, specification of glass, and so on. To analyze the applicability of various solar cells as window-type BIPVs, in this study, we evaluated their applicability, at the current development level, by analyzing the indoor illuminance, heat gain and heat loss; the cooling, heating, and lighting energy levels; and the generation performance of the various solar cells. To enhance the future applicability of window type BIPV, we analyze the overall energy performance of the building, according to changes in visible light transmittance and generation efficiency. The main research results are as follows. The area ratios above the standard illuminance, based on the window type and according to the VLT, were in order of low-e glazing, a-Si window, DSSC window, and c-Si window. The heat gain of the semi-transparent solar cell winodw was remarkably low. The energy consumption of buildings was highest in the order of c-Si window, DSSC window, a-Si window, and clear low-e window. However, in the case of including the power generation performance of the solar cell, the energy consumption was found to be high in order of DSSC window, c-Si window, a-Si window, and clear low-e window. In the future, if a window-type BIPV is developed, we believe that improvement in power generation performance and improvement in visible light transmittance will be needed.

• Convergence Security Journal
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• v.22 no.2
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• pp.99-104
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• 2022

This study provided a prediction model for solar energy production in Yeongam province, Jeollanam-do. The model was derived from the correlation between climate changes and solar power production in Yeongam province, Jeollanam-do, and presented a prediction of solar power generation through the regression analysis of 6 parameters related to weather and solar power generation. The data used in this study were the weather and photovoltaic production data from January in 2016 to December in 2019 provided by public data. Based on the data, the machine learning technique was used to analyzed the correlation between weather change and solar energy production and derived to the prediction model. The model showed that the photovoltaic production can be categorized by the three-stage production index and will be used as an important barometer in the agriculture activity and the use of photovoltaic electricity.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.1-4
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• 2022

The growth in global population and industrialization has led to an increasing demand for electricity. Accordingly, the electricity providers need to increase the electricity generation. Due to the economical and environmental concerns associated with the generation of electricity from fossil fuels. Alternative power recourses that can potentially mitigate the economical and environmental are of interest. Renewable energy resources are promising recourses that can participate in producing power. Among renewable power resources, solar energy is an abundant resource and is currently a field of research interest. Photovoltaic solar power is a promising renewable energy resource. The power output of PV systems is mainly affected by the solar irradiation and ambient temperature. this paper investigates the utilization of machine learning unsupervised neural network techniques that potentially improves the reliability of PV solar power systems during integration into the electrical grid.