• Atmosphere
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• v.34 no.2
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• pp.177-185
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• 2024

As increasing global interest in renewable energy due to the ongoing climate crisis, there is a growing need for efficient technologies to manage such resources. This study focuses on the predictive skill of daily solar power generation using weather observation and forecast data. Meteorological data from the Korea Meteorological Administration and solar power generation data from the Korea Power Exchange were utilized for the period from January 2017 to May 2023, considering both inland (Daejeon) and coastal (Busan) regions. Temperature, wind speed, relative humidity, and precipitation were selected as relevant meteorological variables for solar power prediction. All data was preprocessed by removing their systematic components to use only their residuals and the residual of solar data were further processed with weighted adjustments for homoscedasticity. Four models, MLR (Multiple Linear Regression), RF (Random Forest), DNN (Deep Neural Network), and RNN (Recurrent Neural Network), were employed for solar power prediction and their performances were evaluated based on predicted values utilizing observed meteorological data (used as a reference), 1-day-ahead forecast data (referred to as fore1), and 2-day-ahead forecast data (fore2). DNN-based prediction model exhibits superior performance in both regions, with RNN performing the least effectively. However, MLR and RF demonstrate competitive performance comparable to DNN. The disparities in the performance of the four different models are less pronounced than anticipated, underscoring the pivotal role of fitting models using residuals. This emphasizes that the utilized preprocessing approach, specifically leveraging residuals, is poised to play a crucial role in the future of solar power generation forecasting.

• 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 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.777-784
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• 2017

Energy harvesting through a thermoelectric module normally makes use of the temperature gradient in the system's operational environment. Therefore, it is difficult to obtain the desired output power when the system is subjected to an environment in which a low temperature gradient is generated across the module, because the power generation efficiency of the thermoelectric device is not optimized. The utilization of solar energy, which is a form of renewable energy abundant in nature, has mostly been limited to photovoltaic solar cells and solar thermal energy generation. However, photovoltaic power generation is capable of utilizing only a narrow wavelength band from the sunlight and, thus, the power generation efficiency might be lowered by light scattering. In the case of solar thermal energy generation, the system usually requires large-scale facilities. In this study, a simple and small size thermoelectric power generation system with a solar concentrator was designed to create a large temperature gradient for enhanced performance. A solar tracking system was used to concentrate the solar thermal energy during the experiments and a liquid circulating chiller was installed to maintain a large temperature gradient in order to avoid heat transfer to the bottom of the thermoelectric module. Then, the setup was tested through a series of experiments and the performance of the system was analyzed for the purpose of evaluating its feasibility and validity.

• Current Photovoltaic Research
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• v.4 no.3
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• pp.98-107
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• 2016

Third-generation photovoltaics are of low cost based on solution processes and are targeting a high efficiency. To meet the commercial demand, however, significant improvements of both efficiency and stability are required. In this sense, interfacial engineering can be useful key to solve these issues because trap sites and interfacial energy barrier and/or chemical instability at organic/organic and organic/inorganic interfaces are critical factors of efficiency and stability degradation. Here, we thoroughly review the interfacial engineering strategies applicable to three representative third-generation photovoltaics - organic, perovskite, colloidal quantum dot solar cell devices.

• Journal of the Korean Solar Energy Society
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• v.29 no.1
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• pp.64-69
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• 2009

This paper aims at enhancing the electric production efficiency of photovoltaic(PV) system. The electrical power of PV system is proportional to light intensity on a PV module surface. In this paper, we apply two types of systems to enhance power generation efficiency. First, of all, concentring sunlight using specular surface and one-axis tracking system which traces the sun with vertical direction are applied in this project. From this, we analyze the fixed type method and power generation efficiency.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.53-58
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• 2009

Solar photovoltaic power generation system is judging that by the most suitable new refreshing energy in real condition of our country forward continuously interest for solar photovoltaic power generation system and diffusion me enlarged. Output decline problem is item to consider necessarily and should be verified in continuous interest for solar photovoltaic power generation system are diffusion. Present plan that minimize output decline calculating module liver optimum interval that consider recitation of a poem to reduce output decline by module liver shade by incidence angle consideration and this that occupy most parts among factor because do output of solar photovoltaic power generation system well.

• Journal of the Korean Solar Energy Society
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• v.38 no.1
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• pp.37-44
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• 2018

Solar panels are modules made up of many cells, like the N-type monosilicon, P-type monosilicon, P-type multisilicon, amorphous thin-film silicon, and CIGS solar cells. An efficient photovoltaic (PV) power is important to use to determine what kind of cell types are used because residential solar systems receive attention. In this study, we used 3-type solar panels - such as N-type monosilicon, P-type monosilicon, and CIGS solar cells - to investigate what kind of solar panel on a house or building performs the best. PV systems were composed of 3-type solar panels on the roof with each ~1.8 kW nominal power. N-type monosilicon solar panel resulted in the best power generation when monitored. Capacity Utilization Factor (CUF) and Performance Ratio (PR) of the N-type Si solar panel were 14.6% and 75% respectively. In comparison, N-type monosilicon and CIGS solar panels showed higher performance in power generation than P-type monosilicon solar power with increasing solar irradiance.

• Journal of the Korean Electrochemical Society
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• v.13 no.1
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• pp.1-9
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• 2010

We are presenting an overview of a R&D trend on dye-sensitized solar cells and organic polymer solar cells, which are classified into a next-generation solar cell, and the perspective on their hybridization technology. When considering the competition with inorganic material-base solar cells, especially, these next-generation solar cells need a new hybridization technology, even though it is still at the initial stage. The fusion and hybridization of them will be not only attractive in a new application, but also promising to expect significant progresses in the near future for successful R&D.

• Journal of the Korean Solar Energy Society
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• v.34 no.3
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• pp.57-65
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• 2014

As the environmental regulations is more strengthened, the study of the renewable energy system and waste heat for electricity production is being accelerated. In this study, the performance and power generation rate of solar power generation by using R134a Rankine cycle was analyzed with solar radiation and mass flow rate of R134a. As a result, the maximum and minimum collected heat of solar collector was 20.4 kW and 13.6 kW at October and December, respectively. Besides, the highest generator power was generated at October and it was 0.91 kW/day, while the lowest generator power is occurred at December and it was about 0.85 kW/day.