• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.82-91
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• 2022

In this paper, time series prediction method of photovoltaic power is introduced using seasonal autoregressive integrated moving average (SARIMA). In order to obtain the best fitting model by a time series method in the absence of an environmental sensor, this research was used data below 50% of cloud cover. Three samples were extracted by time intervals from the raw data. After that, the best fitting models were derived from mean absolute percentage error (MAPE) with the minimum akaike information criterion (AIC) or beysian information criterion (BIC). They are SARIMA (1,0,0)(0,2,2)14, SARIMA (1,0,0)(0,2,2)28, SARIMA (2,0,3)(1,2,2)55. Generally parameter of model derived from BIC was lower than AIC. SARIMA (2,0,3)(1,2,2)55, unlike other models, was drawn by AIC. And the performance of models obtained by SARIMA was compared. MAPE value was affected by the seasonal period of the sample. It is estimated that long seasonal period samples include atmosphere irregularity. Consequently using 1 hour or 30 minutes interval sample is able to be helpful for prediction accuracy improvement.

• Korean Journal of Construction Engineering and Management
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• v.16 no.2
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• pp.65-76
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• 2015

The Clean Development Mechanism (CDM) is the multi-lateral 'cap and trade' system endorsed by the Kyoto Protocol. CDM allows developed (Annex I) countries to buy CER credits from New and Renewable (NE) projects of non-Annex countries, to meet their carbon reduction requirements. This in effect subsidizes and promotes NE projects in developing countries, ultimately reducing global greenhouse gases (GHG). To be registered as a CDM project, the project must prove 'additionality,' which depends on numerous factors including the adopted technology, baseline methodology, emission reductions, and the project's internal rate of return. This makes it difficult to determine ex ante a project's acceptance as a CDM approved project, and entails sunk costs and even project cancellation to its project stakeholders. Focusing on hydro power projects and employing UNFCCC public data, this research developed a prediction model using logistic regression and CART to determine the likelihood of approval as a CDM project. The AUC for the logistic regression and CART model was 0.7674 and 0.7231 respectively, which proves the model's prediction accuracy. More importantly, results indicate that the emission reduction amount, MW per hour, investment/Emission as crucial variables, whereas the baseline methodology and technology types were insignificant. This demonstrates that at least for hydro power projects, the specific technology is not as important as the amount of emission reductions and relatively small scale projects and investment to carbon reduction ratios.

• Journal of the Korean Institute of Gas
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• v.22 no.4
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• pp.55-62
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• 2018

There is growing interest in solar power generation due to global warming. As a result, demand for polysilicon, which is the core material for solar cells, is increasing day by day. As the market grows, large and small accidents occurred in the production process. In 2013, hydrochloric acid leaked from the polysilicon manufacturing plant in SangJu. In 2014, a fire occurred at a polysilicon manufacturing plant in Yeosu, and in 2015, STC(Silicon Tetrachloride) leaked at a polysilicon manufacturing plant in Gunsan City. Leakage of chemicals in the polysilicon manufacturing process can affect not only the workplace but also the surrounding area. Therefore, in this study, we identified the hazardous materials used in the polysilicon manufacturing process and quantitatively estimate the amount of leakage and extent of damage when the worst case scenario is applied. As a result, the damage distance by explosion was estimated to be 726 m, and the damage distance to toxicity was estimated to be 4,500 m. And, if TCS(Trichlorosilane), STC(Silicon Tetrachloride), DCS(Dichlorosilane) leaks into the air and reacts with water to generate HCl, the damage distance is predicted to 5.7 km.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.29-42
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• 2023

In this paper, we propose a method to enhance the prediction accuracy of solar irradiance for three major South Korean cities: Seoul, Busan, and Incheon. Our method entails the development of five generative models-vanilla GAN, CTGAN, Copula GAN, WGANGP, and TVAE-to generate independent variables that mimic the patterns of existing training data. To mitigate the bias in model training, we derive values for the dependent variables using random forests and deep neural networks, enriching the training datasets. These datasets are integrated with existing data to form comprehensive solar irradiance prediction models. The experimentation revealed that the augmented datasets led to significantly improved model performance compared to those trained solely on the original data. Specifically, CTGAN showed outstanding results due to its sophisticated mechanism for handling the intricacies of multivariate data relationships, ensuring that the generated data are diverse and closely aligned with the real-world variability of solar irradiance. The proposed method is expected to address the issue of data scarcity by augmenting the training data with high-quality synthetic data, thereby contributing to the operation of solar power systems for sustainable development.

• Proceedings of the KIPE Conference
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• 2018.07a
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• pp.123-125
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• 2018

태양광발전시스템은 낮은 효율의 PV 패널을 사용하여 최대의 전력을 생산하기 위해 PV 패널의 최대전력점에서 운전하는 MPPT(Maximum Power Point Tracking) 제어가 반드시 필요하다. 기존의 MPPT 알고리즘은 대부분 경사법에 기초하고 있으며 그 중 대표적인 방법이 P&O(Perturb and Observe) 알고리즘이다. P&O 알고리즘의 MPPT 성능을 좌우하는 두 가지 인수는 MPPT 제어주기와 변량전압의 크기이다. MPPT 제어기의 빠른 동특성과 극대화된 효율을 위한 최적의 MPPT 제어주기와 변량전압의 크기를 결정하기 위해서는 실제 날씨 환경에서 다양한 일사량 프로파일 패턴에 대한 MPPT 제어기의 성능분석이 필수적이다. 본 논문에서는 대한민국 중부지역의 전형적인 맑은 날씨와 흐린 날씨에서 실제 일사량을 측정하고, 취득한 일사량데이터를 기초로 저자가 개발한 다이오드 등가모델을 적용하여 시뮬레이션을 수행하였다. 이를 기반으로 MPPT 제어주기의 설정값에 따른 PV 패널의 전력생산량을 예측하여 MPPT 목표 효율을 극대화할 수 있는 최적의 MPPT 제어주기를 제시한다.

• Journal of the Korean Solar Energy Society
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• v.39 no.2
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• pp.71-80
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• 2019

Daily ahead forecast is necessary for the electricity balance between load and supply due to the variability renewable energy. Numerical weather prediction is usually employed to produce the solar irradiance as well as electric power forecast for more than 12 hours forecast horizon. UM-LDAPS model is the numerical weather prediction operated by Korea Meteorological Administration and it generates the 36 hours forecast of hourly total irradiance 4 times a day. This study attempts to evaluate the model performance against the in situ measurements at 37 ground stations from January to May, 2013. Relative mean bias error, mean absolute error and root mean square error of hourly total irradiance are averaged over all ground stations as being 8.2%, 21.2% and 29.6%, respectively. The behavior of mean bias error appears to be different; positively largest in Chupoongnyeong station but negatively largest in Daegu station. The distinct contrast might be attributed to the limitation of microphysics parameterization for thick and thin clouds in the model.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.871-874
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• 2010

Needs for Smart Grid development are increasing all over the world as a solution to its problem according to depletion of energy resources, climatic and environmental rapidly change and growing demand for electrical power. Especially decentralized power is attracting world's attention. In this mood a new era for a unit scale of decentralized power environment is on its way in building. However there is a problem to have to be solved in the uniformity of power quality because the amount of power generated from renewable energy resources such as wind power and solar light is very sensitive to climate fluctuation. And thus this paper tries to suggest an energy management method on basis of real time monitoring for meteorological data. In the current situation of lacking in USN-based killer application in Smart Grid field, this paper proposes the USN-based DER management system which collects the meteorological data and control power system througout utilizing wireless sensor network technique this business. This communication technique is regarded to be efficient in aspects of installation cost and tits maintenance cost. The proposed EMS model embodies the method for predicting the power generation by monitoring and analyzing the climatic data and controling the efficient power distribution between the renewable energy and the existing power. The ultimate goal of this paper is to provide the technological basis for achieving zero-energy house.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.71-78
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• 2020

In the case of power generation using renewable energy, power production may not be smooth due to the influence of the weather. The energy storage system (ESS) is used to increase the efficiency of solar and wind power generation. ESS has been continuously fired due to a lack of battery protection systems, operation management, and control system, or careless installation, leading to very big casualties and economic losses. ESS stability and battery protection system operation management technology is indispensable. In this paper, we present a battery level calculation algorithm and a failure prediction algorithm for ESS optimization and stable operation. The proposed algorithm calculates the correct battery level by accumulating the current amount in real-time when the battery is charged and discharged, and calculates the battery failure by using the voltage imbalance between battery cells. The proposed algorithms can predict the exact battery level and failure required to operate the ESS optimally. Therefore, accurate status information on ESS battery can be measured and reliably monitored to prevent large accidents.

• KIEAE Journal
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• v.14 no.1
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• pp.67-74
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• 2014

There have distinctly been no the installation criteria and maintenance management of BIPV systems, although the BIPV market is consistently going on increasing. In addition, consideration of the BIPV generation quantity which has been installed at several diverse places is currently almost behind within region in Korea. Therefore, the main aim of this study is to evaluate the BIPV generation and to be base data of reducing rate depending on regional installation angles using PVpro which was verified by measured data. Various conditions were an angle of inclination and azimuth under six major cities: Seoul, Daejeon, Daegu, Busan, Gwangju, Jeju-si for the BIPV system generation analysis. As the results, Seoul showed the lowest BIPV generation: 1,054kWh/kWp.year, and Jeju-si have 5percent more generation: 1,108.0kWh/kWp.year than Seoul on horizontal plane. Gwangju and Daejeon turned out to have similar generation of result, and Busan showed the highest generation: 1,193.5kWh/kWp.year, which was increased by over 13percent from Seoul on horizontal plane. Another result, decreasing rate of BIPV generation depending on regional included angle indicate that the best position was located on azimuth: $0^{\circ}$(The south side) following the horizontal position(an angle of inclination: $30^{\circ}$). And the direction on a south vertical position(azimuth: $0^{\circ}$, an angle of inclination: $90^{\circ}$) then turned out reducing rate about 40percent compared with the best one. Therefore, these results would be used to identify the installation angle of the BIPV module as an appropriate position.