• Journal of Platform Technology
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• v.6 no.3
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• pp.38-46
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• 2018

Smart Grid is an intelligent power grid for efficiently producing and consuming electricity through bi-directional communication between power producers and consumers. As renewable energy develops, the share of renewable energy in the smart grid is increasing. Renewable energy has a problem that it differs from existing power generation methods that can predict and control power generation because the power generation changes in real time. Applying a self-adaptative framework to the Smart Grid will enable efficient operation of the Smart Grid by adapting to the amount of renewable energy power generated in real time. In this paper, we assume that smart villages equipped with photovoltaic power generation facilities are installed, and apply the self-adaptative framework, AiTES, to show that smart grid can be efficiently operated through self adaptation framework.

• Journal of the Korean Solar Energy Society
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• v.37 no.1
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• pp.81-90
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• 2017

We have analyzed the performance of 58 kWp photovoltaic (PV) power systems installed in Jeddah, Saudi Arabia. Performance ratio (PR) of 3 PV systems with 3 desert-type PV modules using monitoring data for 1 year showed 85.5% on average. Annual degradation rate of 5 individual modules achieved 0.26%, the regression model using monitoring data for the specified interval of one year showed 0.22%. Root mean square error (RMSE) of 6 big data analysis models for power output prediction in May 2016 was analyzed 2.94% using a support vector regression model.

• 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.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.942-945
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• 2010

The development of decentralized power has appeared as part of an effort to decrease the energy loss and the cost for electric power facilities through installing small renewable energy generation systems including solar and wind power generation. Recently a new era for decentralized power environment in building is coming in order to handle the climatic and environmental change occurred all over the world. Especially solar and wind power generation systems can be easily set up and are also economically feasible, and thus many industrial companies enter into this business. This paper suggests the overall architecture for the decentralized renewable power system and the prediction method of power on climatic change. The ultimate goal is to help manage the overall power efficiently and thus provide the technological basis for achieving zero-energy house.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.299-308
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• 2022

Recent, energy transition policies are driving to increase in the number of small photovoltaic(PV) generators. It is difficult for system operators to accurately anticipate the amount of power generated from such small scale PV generation, and this may disrupt dispatch schedules and result in an increase in cost. The need for a Virtual Power Plant(VPP) is emerging as a way of resolving these problems, as it would integrate small-scale PV plants and eliminate uncertainty about the amount of power generated, control voltage, and provide power reserves. In this paper, the cost evaluation methods are described for determination of VPP cloud charges both Net Present Value(NPV) method and Profitability Index(PI) method, the calculated outcomes of the two types of cost evaluation methods are presented in detail. It seems we secure profitability as we get 1.22 of profitability index from calculation results, it may be attractive for the aggregator as NPV is enough for satisfying profitability.

• 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.

• Journal of Digital Convergence
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• v.12 no.3
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• pp.211-218
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• 2014

Recently, wind energy is expanding to combination of computing to forecast of wind power generation as well as intelligent of wind powerturbine. Wind power is rise and fall depending on weather conditions and difficult to predict the output for efficient power production. Wind power is need to reliably linked technology in order to efficient power generation. In this paper, distributed power generation forecasts to enhance the predicted and actual power generation in order to minimize the difference between the power of distributed power short-term prediction model is designed. The proposed model for prediction of short-term combining the physical models and statistical models were produced in a physical model of the predicted value predicted by the lattice points within the branch prediction to extract the value of a physical model by applying the estimated value of a statistical model for estimating power generation final gas phase produces a predicted value. Also, the proposed model in real-time National Weather Service forecast for medium-term and real-time observations used as input data to perform the short-term prediction models.

• Journal of the Korean Solar Energy Society
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• v.39 no.3
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• pp.81-89
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• 2019

Long-term solar irradiance data are required for reliable performance evaluation and feasibility analysis of solar photovoltaic systems. However, measurement data of the global horizontal irradiance (GHI) are only available for major cities in Korea. Neither the direct normal irradiance (DNI) nor the diffuse horizontal irradiance (DHI) are available, which are also needed to calculate the irradiance on the tilted surface of PV array. It is a simple approach to take advantage of the decomposition model that extracts DNI and DHI from GHI. In this study, we investigate variations of solar PV power estimation due to the choice of decomposition model. The GHI data from Korea Meteorological Administration (KMA) were used and different sets of typical meteorological year (TMY) data using some well-known decomposition models were generated. Then, power outputs with the different TMY data were calculated, and a variation of 3.7% was estimated due to the choice of decomposition model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.113-118
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• 2019

A solar power plant is a facility that produces electricity. As the risk of fire and electric shock accidents is diversified, the risk of workers, surrounding people, and facilities is increased, preventing safety accidents and promptly responding to safety accidents Is emerging. In light of the necessity of such development, it is necessary to develop a solar power generation management system that can diagnose and maintain the problems of the power generation system in real time by developing technologies for collecting and analyzing the data produced by the solar power generation system As a result, the utilization rate and the maintenance cost can be reduced. In order to do this, it is necessary to accurately predict the solar power generation amount in the present state, to diagnose the abnormality of the current power generation state and to grasp the abnormal position, and to use the model considering economical efficiency when the abnormal position is grasped, And the time and other information should be provided.