• Journal of Integrative Natural Science
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• v.12 no.1
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• pp.20-23
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• 2019

Demand response is usually operated through using the power rates and incentives. Demand management based on power charges is the most rational and efficient demand management method, and such methods include rolling base charges with peak time, sliding scaling charges depending on time, sliding scaling charges depending on seasons, and nighttime power charges. Search for other methods to stimulate resources on demand by actively deriving the demand reaction of loads to increase the energy efficiency of loads. In this paper, ESS algorithm for saving energy based on predicting the amount of solar power generation that can be used for buildings with small loads not under electrical grid.

• Plant Journal
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• v.17 no.4
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• pp.41-52
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• 2021

In this study, the PV output and module temperature values were predicted using the Meteorological Agency data and compared with actual data, weather, solar radiation, ambient temperature, and wind speed. The forecast accuracy by weather was the lowest in the data on a clear day, which had the most data of the day when it was snowing or the sun was hit at dawn. The predicted accuracy of the module temperature and the amount of power generation according to the amount of insolation decreased as the amount of insolation increased, and the predicted accuracy according to the ambient temperature decreased as the module temperature increased as the ambient temperature increased and the amount of power generated lowered the ambient temperature. As for wind speed, the predicted accuracy decreased as the wind speed increased for both module temperature and power generation, but it was difficult to define the correlation because wind speed was insignificant than the influence of other weather conditions.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.39-44
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• 2023

Renewable energy such as solar and wind power is a resource that is sensitive to weather conditions and environmental changes. Since the amount of power generated by a facility can vary depending on the installation location and structure, it is important to accurately predict the amount of power generation. Using meteorological data, a data preprocessing process based on principal component analysis was conducted to monitor the relationship between features that affect energy production prediction. In addition, in this study, the prediction was tested by reconstructing the dataset according to the sensitivity and applying it to the machine learning model. Using the proposed model, the performance of energy production prediction using random forest regression was confirmed by predicting energy production according to the meteorological environment for new and renewable energy, and comparing it with the actual production value at that time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.514-517
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• 2017

In solar photovoltaic systems, power generation is greatly affected by the weather conditions, so it is essential to predict solar energy for stable load operation. Therefore, data on weather conditions are needed as inputs to machine learning algorithms for solar energy prediction. In this paper, we use 15 kinds of weather data such as the precipitation accumulated during the 3 hours of the surface, upward and downward longwave radiation average, upward and downward shortwave radiation average, the temperature during the past 3 hours at 2 m above from the ground and temperature from the ground surface as input data to the algorithm. We analyzed the statistical characteristics and correlations of weather data and extracted the downward and upward shortwave radiation averages as a major elements of a feature vector with high correlation of 70% or more with solar energy.

• Journal of Convergence Society for SMB
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• v.6 no.4
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• pp.93-98
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• 2016

Recently, the project related to the smart grid are being actively studied around the developed world. In particular, the long-term stabilization measures distributed power supply problem has been highlighted. In this paper, we propose a three-dimensional numerical weather prediction models to compare the error rate information which combined with the physical models and statistical models to predict the output of distributed power. Proposed model can predict the system for a stable power grid-can improve the prediction information of the distributed power. In performance evaluation, proposed model was a generation forecasting accuracy improved by 4.6%, temperature compensated prediction accuracy was improved by 3.5%. Finally, the solar radiation correction accuracy is improved by 1.1%.

• The Journal of Society for e-Business Studies
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• v.24 no.1
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• pp.1-16
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• 2019

Recently, since responding to meteorological changes depending on increasing greenhouse gas and electricity demand, the importance prediction of photovoltaic power (PV) is rapidly increasing. In particular, the prediction of PV power generation may help to determine a reasonable price of electricity, and solve the problem addressed such as a system stability and electricity production balance. However, since the dynamic changes of meteorological values such as solar radiation, cloudiness, and temperature, and seasonal changes, the accurate long-term PV power prediction is significantly challenging. Therefore, in this paper, we propose PV power prediction model based on deep learning that can be improved the PV power prediction performance by learning to use meteorological and seasonal information. We evaluate the performances using the proposed model compared to seasonal ARIMA (S-ARIMA) model, which is one of the typical time series methods, and ANN model, which is one hidden layer. As the experiment results using real-world dataset, the proposed model shows the best performance. It means that the proposed model shows positive impact on improving the PV power forecast performance.

• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.561-569
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• 2019

Solar photovoltaic can provide electrical energy with only radiation, and its use is expanding rapidly as a new energy source. This study predicts the short and long-term PV power generation using actual converter output data of photovoltaic system. The prediction algorithm uses multiple linear regression, support vector machine (SVM), and deep learning such as deep neural network (DNN) and long short-term memory (LSTM). In addition, three models are used according to the input and output structure of the weather element. Long-term forecasts are made monthly, seasonally and annually, and short-term forecasts are made for 7 days. As a result, the deep learning network is better in prediction accuracy than multiple linear regression and SVM. In addition, LSTM, which is a better model for time series prediction than DNN, is somewhat superior in terms of prediction accuracy. The experiment results according to the input and output structure appear Model 2 has less error than Model 1, and Model 3 has less error than Model 2.

• Journal of the Korean Solar Energy Society
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• v.39 no.6
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• pp.93-112
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• 2019

In this paper, a very new web-based software for renewable energy system (RES) design and economic evaluation was introduced. This solution would provide the precise RES estimation service including not only photovoltaic (PV), wind turbine (WT) and fuel cell (FC) individually but also energy storage system (ESS) as combined forms with PV or WT. The three reasons why we ought to develop it are: First, the standardized tool suitable to the domestic environment for estimating power generation from RES facilities and economic evaluation is required. Secondly, the standardized tool is needed to spread domestic RES supply policy and to promote the new industry in the micro-grid field. The last, the reliability of economic evaluation should be enhanced more for new facilities. To achieve those aims, the weather database of one hundred locations have established and the RES facility database has also constructed. For the energy management, mathematical models for PV, WT, ESS and FC were developed. As a final phase, the analytical process to evaluate economics has performed with field data verification.

• Journal of Platform Technology
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• v.6 no.3
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• pp.47-54
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• 2018

Predictive analysis in the hardware industry can be performed at an appropriate point in time to prevent failure of production facilities and reduce management costs. This helps to perform more efficient and scientific maintenance through automation of failure analysis. Among them, predictive management aims to prevent the occurrence of anomalous state by identifying and improving the abnormal state based on the gathering, analysis, and scientific data management of facilities using information technology and constructing prediction model do. In this study, we made a fault tree through the Relex tool and analyzed the error code of the hardware to study the safety.

• Journal of KIBIM
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• v.5 no.4
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• pp.53-62
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• 2015

Korea has been at the forefront of green growth initiatives. In 2008, the government declared the new vision toward 'low-carbon society and green growth'. The government subsidies and Feed-in Tariff (FIT) increased domestic usage of solar power by supplying photovoltaic housing and photovoltaic generation systems. Since 2000, solar power industry has been the world's fastest growing source with the annual growth rate of 52.5%. Especially, BIPV(Building Integrated Photovoltaic) systems are capturing a growing portion of the renewable energy market due to several reasons. BIPV consists of photovoltaic cells and modules integrated into the building envelope such as a roof or facades. By avoiding the cost of conventional materials, the incremental cost of photovoltaics is reduced and its life-cycle cost is improved. When it comes to atypical building, numerous problems occur because PV modules are flat, stationary, and have its orientation determined by building surface. However, previous studies mainly focused on improving installations of solar PV technologies on ground and rooftop photovoltaic array and developing prediction model to estimate the amount of produced electricity. Consequently, this paper discusses the problem during a planning and design stage of BIPV systems and suggests the method to select optimal design of the systems by applying the national strategy and economic policies. Furthermore, the paper aims to develop BIM tool based on the engineering knowledge from experts in order for non-specialists to design photovoltaic generation systems easily.