• Proceedings of the KSME Conference
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• 2003.04a
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• pp.1542-1547
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• 2003

This paper represents the solar collector performance with type of an evacuate double glass, and a copper tube was installed in center of collector to get a solar thermal energy. The one module of solar collector and artificial sun were used in this experiment The distance between artificial sun and solar collector was fixed at 0.5m, and this experimental condition was focused on winter season. The experiments were carried out. three times for getting a accurate data and the heat amount of one module evacuate d solar collector was estimated at out. 48 kcal/hr.

• Journal of The Korean Astronomical Society
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• v.36 no.2
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• pp.67-72
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• 2003

The basic objective of helioseismology is to determine the structure and the dynamics of the Sun by analysing the frequency spectrum of the solar oscillations. Accurate frequency measurements provide information that enables us to probe the solar interior structure and the dynamics. Therefore the frequency of the solar oscillation is the most fundamental and important information to be extracted from the solar oscillation observation. This is why many efforts have been put into the development of accurate data analysis techniques, as well as observational efforts. To test one's data analysis method, a realistic artificial data set is essential because the newly suggested method is calibrated with a set of artificial data with predetermined parameters. Therefore, unless test data sets reflect the real solar oscillation data correctly, such a calibration is likely incomplete and a unwanted systematic bias may result in. Unfortunately, however, commonly used artificial data generation algorithms insufficiently accommodate physical properties of the stochastic excitation mechanism. One of reason for this is that it is computaionally very expensive to solve the governing equation directly. In this paper we discuss the nature of solar oscillation excitation and suggest an efficient algorithm to generate the artificial solar oscillation data. We also briefly discuss how the results of this work can be applied in the future studies.

• Current Photovoltaic Research
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• v.7 no.4
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• pp.85-96
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• 2019

Renewable energy is emerging as a reliable alternative source of energy, it is much safer, cleaner than conventional sources and has contributed significantly in this sector. However, there are still some challenges that needed to address this evolving technology. Artificial Intelligence (A. I.) can assess the past, optimize the present, and forecast the future. Therefore, A. I. will resolve most of these problems. Artificial intelligence is complex in nature, but it reduces error and aims to reach a greater degree of precision which make renewables smarter. This paper provides an overview of frequently used A. I. methods in solar energy applications. A sample algorithm is also provided for literature purposes and knowledge transfer.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.36 no.3
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• pp.261-265
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• 2023

In this paper, we analyzed the transformation of the power following by the angle of incidence of the solar, the angle of photovoltaic module and artificial solar changed from 30° to 90° and synchronously changed the distance from 0.1 m to 0.5 m. Setting the distance between the artificial solar and the luminometer from 0.1 m to 0.5 m and set the angles to 90°, 60°, 45°, and 30°, the angle was 90° and when the distance was 0.1 m, the maximum Illuminance was 19,580 lux, the light could be obtained more. If the angle of incidence between the Artificial solar and the photovoltaic module was 90° and the variable resistance was 1,000 Ω at a distance of 0.4 m, the maximum power reached 0.82 W. Provided that the angle of incidence between the artificial solar and the photovoltaic module was 90° and the distance was 0.2 m since the variable resistance had the maximum power of 500 Ω, the maximum power was 0.78 W. At 1,000 Ω, the maximum power is 0.80 W so the maximum power at the variable resistance 1,000 Ω could obtain higher power than the variable resistance 500 Ω. The variable resistance was 1,000 Ω and the angle of incidence between the Artificial solar and the photovoltaic module was 90° at a distance of 0.4 m, and the maximum power reached 0.82 W. The angle was 60° at 0.3 m and 0.4 m the maximum power reached 0.10 W. The angle was 45° at 0.2 m maximum power reached 0.020 W, the angle was 30° at 0.4 m, and the maximum power reached 0.004 W. In four results about maximum power depending on the angle of incidence between the artificial solar and the photovoltaic module, the luminous efficiency and maximum power can be got the best at an angle of 90°.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.354-356
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• 2011

Renewable energy resources such as wind, wave, solar, micro hydro, tidal and biomass etc. are becoming importance stage by stage because of considering effect of the environment. Solar energy is one of the most successful sources of renewable energy for the production of electrical energy following solar energy. And, the solar/photovoltaic cell generators depend on the solar radiation, which is a random variable so this poses difficulty in the system scheduling and energy dispatching, as the schedule of the photovoltaic cell generators availability is not known in advance. This paper proposes to use the two-layered artificial neural networks for predicting the actual solar radiation from the previous values of the same variable.

• Structural Engineering and Mechanics
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• v.83 no.2
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• pp.259-272
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• 2022

The branch of electronics that uses an organic solar cell or conductive organic polymers in order to yield electricity from sunlight is called photovoltaic. Regarding this crucial issue, an artificial intelligence-based predictor is presented to investigate the vibrational behavior of the organic solar cell. In addition, the generalized differential quadrature method (GDQM) is utilized to extract the results. The validation examination is done to confirm the credibility of the results. Then, the deep neural network with fully connected layers (DNN-FCL) is trained by means of Adam optimization on the dataset whose members are the vibration response of the design-points. By determining the optimum values for the biases along with weights of DNN-FCL, one can predict the vibrational characteristics of any organic solar cell by knowing the properties defined as the inputs of the mentioned DNN. To assess the ability of the proposed artificial intelligence-based model in prediction of the vibrational response of the organic solar cell, the authors monitored the mean squared error in different steps of the training the DNN-FCL and they observed that the convergency of the results is excellent.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.110-115
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• 2022

Accurate current-voltage modeling of solar cell systems plays an important role in power prediction. Solar cells have nonlinear characteristics that are sensitive to environmental conditions such as temperature and irradiance. In this paper, the output characteristics of photovoltaic module are accurately predicted by combining the artificial neural network and physical model. In order to estimate the performance of PV module under varying environments, the artificial neural network model is trained with randomly generated temperature and irradiance data. With the use of proposed model, the current-voltage and power-voltage characteristics under real environments can be predicted with high accuracy.

• Journal of Biosystems Engineering
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• v.43 no.4
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• pp.352-361
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• 2018

Purpose: The objective of this study was to develop a multilayer perceptron (MLP) model to estimate solar radiation using a solar module. Methods: Data for the short-circuit current of a solar module and other environmental parameters were collected for a year. For MLP learning, 14,400 combinations of input variables, learning rates, activation functions, numbers of layers, and numbers of neurons were trained. The best MLP model employed the batch backpropagation algorithm with all input variables and two hidden layers. Results: The root-mean-squared error (RMSE) of each learning cycle and its average over three repetitions were calculated. The average RMSE of the best artificial neural network model was $48.13W{\cdot}m^{-2}$. This result was better than that obtained for the regression model, for which the RMSE was $66.67W{\cdot}m^{-2}$. Conclusions: It is possible to utilize a solar module as a power source and a sensor to measure solar radiation for an agricultural sensor node.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.22 no.11
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• pp.812-819
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• 2010

Recently residential buildings are characterized with high-rise and high density. Under this circumstance, achieving comfortable and healthy indoor environment with minimized energy consumption becomes a very challenging engineering and societal issue. Along this the increased size and transparency of window as well as light surface caused by high stories lowers the heat shield efficiency of building. Since glass that constitutes building surface has low heat efficiency, it aggravates heat loss of all building considerably, thereby resulting in extreme heating load and cooling load in the country where temperature varies much in summer and winter. The research will check whether experiment can be effectively done by overcoming the limit of existing artificial solar laboratory constructed in the country and properly adjusting controlled variables with simplified function through construction of this experimental set.

• Solar Energy
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• v.1 no.1
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• pp.30-36
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• 1981

Pool Boiling heat transfer from controlled arrays of artificial nucleation sites was studied experimentally. Distilled water were boiled from artificial sites of uniform size, shape and spacing, drilled in superfinished copper horizontal surfaces at site density of 16, 25, 36, 49, 64, 81, 100 per $2.25cm^2$. The results confirm the boiling heat transfer from artificial sites can be improved by increasing the site density N/A or temperature difference ${\Delta}T$ or both. Following experimental correlation were developed for predicting the heat transfer rate from the heating surface which has artificial sites. $$q/A = C(T_s - T_{sat})^{1.811}(N/A)^{0.41}$$