• Journal of the Korean Solar Energy Society
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• v.39 no.2
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• pp.11-22
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• 2019

The first case of the Central Environmental Dispute Mediation Committee, which recently decided to repay the builder for damaging the solar power plant due to the obstruction of the sunshine of new buildings, came out. Even if the Respondent complies with the provisions of the Building Act, the decision of the Complainant can be considered to have been made in light of the fact that the applicant's power plant has suffered from sunlight damage. However, since the extent of the damage may differ depending on the weather, the decision is reserved, and there is room for additional disputes on a regular basis because the loss of power generation to be continuously generated is not reflected in the future. Therefore, in this study, we try to find the direction of dispute adjustment by summarizing the issues related to the generation of power generation due to the influence of shading through the analysis of the case of dispute related to sunlight related to the PV system.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.10
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• pp.95-100
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• 2014

To solve the problem of conventional fossil energy, utilization of renewable energy is growing rapidly. Solar energy as an energy source is infinite, and a variety of research is being conducted into its utilization. To change solar energy into electrical energy, we need to build a solar power plant. The efficiency of such a plant is strongly influenced by meteorological factors; that is, its efficiency is determined by solar radiation. However, when analyzing observed generation data, it is clear that the generated amount is changed by various factors such as weather, location and plant efficiency. In this paper, we proposed a solar power generation prediction algorithm using geographical factors such as latitude and elevation. Hence, changes in generated amount caused by the installation environment are calculated by curve fitting. Through applying the method to calculate this generation amount, the difference between real generated amount is analyzed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.219-224
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• 2019

The number of solar power installation companies including domestic small scale (50kW or less) is increasing rapidly, but the efficient operation system and management are insufficient. Therefore, a new type of operating system is needed as a maintenance management aspect to maximize the generation amount in the current state rather than the additional function which causes the increase of the generation cost. In this paper, we utilize Big Data and sensor network to maximize the operating efficiency of solar power plant and analyze the expert system to develop power generation prediction technology, module unit fault detection technology, life prediction of inverter components and report technology, maintenance optimization And to develop a smart monitoring system that enables optimal operation of photovoltaic power plants such as development of algorithms and economic analysis.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.3
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• pp.223-228
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• 2019

In order to integrate large amounts of variable generation resources such as wind and solar reliably into power grids, accurate renewable energy forecasting is necessary. Since renewable energy generation output is heavily influenced by environmental variables, accurate forecasting of power generation requires meteorological data at the point where the plant is located. Therefore, a spatial approach is required to predict the meteorological variables at the interesting points. In this paper, we propose the meteorological variable prediction model for enhancing renewable generation output forecasting model. The proposed model is implemented by three geostatistical techniques: Ordinary kriging, Universal kriging and Co-kriging.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.4_2
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• pp.715-724
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• 2023

Solar energy forecasting is essential for (1) power system planning, management, and operation, requiring accurate predictions. It is crucial for (2) ensuring a continuous and sustainable power supply to customers and (3) optimizing the operation and control of renewable energy systems and the electricity market. Recently, research has been focusing on developing solar energy forecasting models that can provide daily plans for power usage and production and be verified in the electricity market. In these prediction models, various data, including solar energy generation and climate data, are chosen to be utilized in the forecasting process. The most commonly used climate data (such as temperature, relative humidity, precipitation, solar radiation, and wind speed) significantly influence the fluctuations in solar energy generation based on weather conditions. Therefore, this paper proposes a hybrid forecasting model by combining the strengths of the Prophet model and the GRU model, which exhibits excellent predictive performance. The forecasting periods for solar energy generation are tested in short-term (2 days, 7 days) and medium-term (15 days, 30 days) scenarios. The experimental results demonstrate that the proposed approach outperforms the conventional Prophet model by more than twice in terms of Root Mean Square Error (RMSE) and surpasses the modified GRU model by more than 1.5 times, showcasing superior performance.

• Journal of the Korean Society for Railway
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• v.20 no.4
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• pp.474-481
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• 2017

When designing photovoltaic power plants in Korea, the prediction of photovoltaic power generation at the design phase is carried out using PVSyst, PVWatts (Overseas power generation prediction software), and overseas weather data even if the test site is a domestic site. In this paper, for a comparative study to predict power generation using weather information, domestic photovoltaic power plants in two regions were selected as target sites. PVsyst, which is a commercial power generation forecasting program, was used to compare the accuracy between the predicted value of power generation (obtained using overseas weather information (Meteonorm 7.1, NASA-SSE)) and the predicted value of power generation obtained by the Korea Meteorological Administration (KMA). In addition, we have studied ways to improve the prediction of power generation through comparative analysis of meteorological data. Finally, we proposed a revised solar power generation prediction model that considers climatic factors by considering the actual generation amount.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.4
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• pp.246-250
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• 2015

Solar energy will be an increasingly important part of power generation because of its ubiquity abundance, and sustainability. To manage effectively solar energy to power system, it is essential part In this paper, we develop the PV power prediction algorithm using adaptive neuro-fuzzy model considering various input factors such as temperature, solar irradiance, sunshine hours, and cloudiness. To evaluate performance of the proposed model according to input factors, we performed various experiments by using real data.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.1
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• pp.1-6
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• 2015

Factors that have influence on solar power generation are specified into three aspects such as meteorological, geographical factors as well as equipment installation. Meteorological factors influence the most among the three. Insolation, sunshine hours, and cloud directly influence on solar power generation, whereas temperature and wind speed have impacts on equipment installation. This paper provides explanation over temperature-wind speed equation by calculating influence of temperature and wind speed on equipment installation. In order to conduct a research, pyranometer, anemometer, air thermometer, module thermometer are installed in 2MWp solar power plant located in South Cholla province, so that real-time meteorological data and generating amount can be analyzed through monitoring system. Besides, if existing and new methods are applied together, accuracy of prediction for generating amount is improved.

• Journal of the Korean Solar Energy Society
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• v.39 no.3
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• pp.59-66
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• 2019

New and renewable energy forecasts are key technology to reduce the annual operating cost of new and renewable facilities, and accuracy of forecasts is paramount. In this study, we intend to build a model for the prediction of short-term solar power generation for 1 hour to 3 hours. To this end, this study applied two time series technique, ARIMA model without considering seasonality and SARIMA model with considering seasonality, comparing which technique has better predictive accuracy. Comparing predicted errors by MAE measures of solar power generation for 1 hour to 3 hours at four locations, the solar power forecast model using ARIMA was better in terms of predictive accuracy than the solar power forecast model using SARIMA. On the other hand, a comparison of predicted error by RMSE measures resulted in a solar power forecast model using SARIMA being better in terms of predictive accuracy than a solar power forecast model using ARIMA.