• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.1036-1043
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• 2014

The installation of PV system to the power distribution system is being increased as one of solutions for environmental pollution and energy crisis. Because the output efficiency of PV system is getting decreased because of the aging phenomenon and several operation obstacles, the technology development of output prediction and state diagnosis of PV modules are required in order to improve operation performance of PV modules. The conventional methods for output prediction by considering various parameters and standard test condition values of PV modules may have difficult and complex computation procedure and also their prediction values may produce large error. To overcome these problems, this paper proposes an optimal prediction algorithm and state diagnosis algorithm of PV modules by using least square methods of linear regression analysis. In addition, this paper presents a state diagnosis evaluation system of PV modules based on the proposed optimal algorithms of PV modules. From the simulation results of proposed evaluation system, it is confirmed that the proposed algorithms is a practical tool for state diagnosis of PV modules.

• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.620-626
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• 2020

This research propose and simulate a solar power generation system monitoring system based on Modbus TCP communication using RaspberryPi, an IOT equipment, as a master and an inverter as a slave. In this model, various sensors are added to the RaspberryPi to add necessary information for monitoring solar power plants, and power generation prediction and monitoring information are transmitted to the smart phone through real-time power generation prediction. In addition, information that is continuously generated by the solar power plant is built on the server as big data, and a deep learning model for predicting power generation is trained and updated. As a result of the study, stable communication was possible based on Modbus TCP with the Raspberry Pi in the inverter, and real-time prediction was possible with the deep learning model learned in the Raspberry Pi. The server was able to train various deep learning models with big data, and it was confirmed that LSTM showed the best error with a learning error of 0.0069, a test error of 0.0075, and an RMSE of 0.0866. This model suggested that it is possible to implement a real-time monitoring system that is simpler, more convenient, and can predict the amount of power generation for inverters of various manufacturers.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.579-587
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• 2016

To obtain a stable energy supply and manage PhotoVoltaic (PV) systems efficiently, satellite imagery methods are being developed to estimate the solar PV potential. This study analyzed trends in the use of satellite imagery in solar PV and solar irradiation estimation technology. The imaging technology is used to produce solar energy resource maps. The trend analysis showed that the level of solar PV technology in Korea is 30% below that of advanced countries. It is impossible to raise such low-level technologies to the levels of advanced countries quickly. Intensive research and development is the only way to achieve the 80% technology level of advanced countries. The information produced in this process can contribute to the management of solar power plants. A valid technology development strategy would be to obtain effective data that can be used for fieldwork. Such data can be produced by estimating solar irradiation very accurately with several-hundred-meter resolution using Communication, Ocean, and Meteorological Satellites (COMS) and next-generation GEO-KOMPSAT 2A, developing core technologies for short- and medium-term irradiation prediction, and developing technologies for estimating the solar PV potential.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.3
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• pp.591-596
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• 2012

Recently, the efficient management system for photovoltaic power plants has been required due to the continuously increasing construction of photovoltaic power plants. In this paper, we propose a cloud-based intelligent management system for many photovoltaic power plants. The proposed system stores the measured data of power plants using Hadoop HBase which is a column-oriented database, and processes the calculations of performance, efficiency, and prediction the amount of power generation by parallel processing based on Map-Reduce model. And, Web-based data visualization module allows the administrator to provide information in various forms.

• Proceedings of the KIPE Conference
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• 2015.07a
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• pp.277-278
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• 2015

태양광발전소를 설치하기 위한 경제적 타당성을 분석하는 경우 기상청에서 제공하는 해당지역의 날씨정보를 기반으로 하는 PV Cell의 연간 발전량 예측 및 분석이 중요한 변수가 된다. 또한 날씨 조건에 대한 PV 발전의 예측은 기 설치되어 운전중에 있는 태양광발전소의 고장진단 및 성능평가에도 사용될 수 있다. 본 논문은 다양한 날씨 조건 중 주변온도, 풍속, 일사량에 따른 PV Cell의 특성을 분석하고, 실시간으로 변화하는 날씨환경에 대하여 순시적으로 PV Cell의 출력특성을 정확히 예측할 수 있는 모델을 수립한다.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.49-62
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• 2023

Solar energy, which is rapidly increasing in proportion, is being continuously developed and invested. As the installation of new and renewable energy policy green new deal and home solar panels increases, the supply of solar energy in Korea is gradually expanding, and research on accurate demand prediction of power generation is actively underway. In addition, the importance of solar radiation prediction was identified in that solar radiation prediction is acting as a factor that most influences power generation demand prediction. In addition, this study can confirm the biggest difference in that it attempted to predict solar radiation using medium-term forecast weather data not used in previous studies. In this paper, we combined the multi-linear regression model, KNN, random fores, and SVR model and the clustering technique, K-means, to predict solar radiation by hour, by calculating the probability density function for each cluster. Before using medium-term forecast data, mean absolute error (MAE) and root mean squared error (RMSE) were used as indicators to compare model prediction results. The data were converted into daily data according to the medium-term forecast data format from March 1, 2017 to February 28, 2022. As a result of comparing the predictive performance of the model, the method showed the best performance by predicting daily solar radiation with random forest, classifying dates with similar climate factors, and calculating the probability density function of solar radiation by cluster. In addition, when the prediction results were checked after fitting the model to the medium-term forecast data using this methodology, it was confirmed that the prediction error increased by date. This seems to be due to a prediction error in the mid-term forecast weather data. In future studies, among the weather factors that can be used in the mid-term forecast data, studies that add exogenous variables such as precipitation or apply time series clustering techniques should be conducted.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.389-392
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• 2012

에너지관리 지원 서비스는 공장 내에서 일어나는 전력발전 및 전력할당을 데어터 분석 기법 등을 이용하여 효과적으로 관리하는 것을 목적으로 한다. 특히 그 중에서도 태양광, 풍력 등 친환경 에너지를 이용한 에너지관리 시스템은 비용절감 뿐만 아니라 환경보호 측면에서도 중요한 문제라 할 수 있다. 이들 친환경 에너지를 제대로 이용하기 위해서는 그들의 발전량을 정확히 예측할 필요가 있지만 현재의 시스템에는 가장 기본적인 예측법인 최근접 이웃법을 사용하고 있다. 최근접 이웃법의 경우 노이즈와 아웃라이어에 민감하다는 단점이 있기 때문에 이들 상황에 대처할 수 있는 보다 정교한 예측법이 필요하다.

• 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.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.1005-1013
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• 2017

In this paper, we investigate the performances of time series models to forecast irradiance that consider weather variables such as temperature, humidity, cloud cover and Global Horizontal Irradiance. We first introduce the time series models and show that regression ARIMAX has the best performance with other models such as ARIMA and multiple regression models.

• 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.