• 한국철도학회논문집
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• 제20권4호
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• pp.474-481
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• 2017

국내에서 태양광 발전설비 설계 시 설계 단계에서의 태양광발전소의 발전량 예측은 국내 현장임에도 불구하고 PVsyst, PVWatts 등 해외 발전량 예측 프로그램과 해외 기상 자료를 이용하여 발전량을 예측하는 경우가 대부분을 차지하고 있는 실정이다. 본 논문에서는 기상정보를 활용한 발전량 예측 비교 연구를 위하여 현재 운영중인 2개 지역의 국내 태양광발전소를 대상지로 선정하였다. 발전량 예측 프로그램인 PVsyst를 활용하여 Meteonorm 7.1과 NASA-SSE의 해외 기상정보를 이용한 발전량 예측값과 국내 기상청 (Korea Meteorology Administration) 기상정보를 활용한 발전량 예측 정확성을 비교하였다. 추가적으로, 기상자료 비교 분석을 통한 발전량 예측 개선 방안을 연구하고, 최종적으로 실제 발전량과의 비교 분석을 통해 기후요소가 고려된 태양광 발전량 예측 수정 모델을 제시하였다.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제54권4호
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• pp.188-194
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• 2005

There are many factors that affect on the system output of Photovoltaic(PV) power generation; the variation of solar radiation, temperature, energy conversion efficiency of solar cell etc. This paper suggests a methodology for calculation of PV generation output using the probability distribution function of irradiance, PV array efficiency and revision factors of solar cell conversion efficiency. Long-term irradiance data recorded every hour of the day for 11 years were used. For goodness-fit test, several distribution (unctions are tested by Kolmogorov-Smirnov(K-S) method. The calculated generation output with or without revision factors of conversion efficiency is compared with that of CMS (Centered Monitoring System), which can monitor PV generation output of each PV generation site.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 A
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• pp.548-550
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• 2004

This paper suggests a methodology for calculation of photovoltaic(PV) generation system output using probability distribution function, PV way efficiency and PV system design Parameters. Long term irradiance recorded for every hour of the day for 11 years were used. For goodness-fit test, several distribution functions are tested by Kolmogorov- Smirnov(K-S) test. And the calculated generation output is compared with that of CMS(Centered Monitoring System), which can monitoring PV generation output of each PV generation site.

• Journal of Electrical Engineering and Technology
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• 제13권5호
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• pp.1874-1885
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• 2018

The customer side operation is getting more complex in a smart grid environment because of the adoption of renewable resources. In performing energy management planning or scheduling, it is essential to forecast non-controllable resources accurately and robustly. The PV system is one of the common renewable energy resources in customer side. Its output depends on weather and physical characteristics of the PV system. Thus, weather information is essential to predict the amount of PV system output. However, weather forecast usually does not include enough solar irradiation information. In this study, a PV system power output prediction model (PPM) under limited weather information is proposed. In the proposed model, meteorological radiation model (MRM) is used to improve cloud cover radiation model (CRM) to consider the seasonal effect of the target region. The results of the proposed model are compared to the result of the conventional CRM prediction method on the PV generation obtained from a field test site. With the PPM, root mean square error (RMSE), and mean absolute error (MAE) are improved by 23.43% and 33.76%, respectively, compared to CRM for all days; while in clear days, they are improved by 53.36% and 62.90%, respectively.

• 전기학회논문지
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• 제64권1호
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• pp.41-47
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• 2015

In recent years, energy supply cases to take advantage of EMS(Energy Management System) are increasing according to high interest of energy efficiency. The important factor for essential and economical EMS operation is the supply and demand plan the hourly power demand of building load using the hierarchical clustering method of variety statistical techniques, and use the real historical data of target load. Also the estimated results of study are obtained the reliability through separate tests of validity.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2024년도 제69차 동계학술대회논문집 32권1호
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• pp.31-34
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• 2024

태양광 에너지는 탄소 중립 이행을 위한 주요 방안으로 많은 주목을 받고 있다. 태양광 발전량은 여러 환경적 요인에 따라 크게 달라질 수 있으므로, 정확한 발전량 예측은 전력 네트워크의 안정성과 효율적인 에너지 관리에 근본적으로 중요하다. 대표적인 인공지능 기술인 신경망(Neural Network)은 불안정한 환경 변수와 복잡한 상호작용을 효과적으로 학습할 수 있어 태양광 발전량 예측에서 우수한 성능을 도출하였다. 하지만, 신경망은 모델의 구조나 초매개변수(Hyperparameter)를 최적화하는 것은 복잡하고 시간이 많이 드는 작업이므로, 에너지 분야에서 실제 산업 적용에 한계가 존재한다. 본 논문은 2단계 신경망 최적화를 통한 태양광 발전량 예측 기법을 제안한다. 먼저, 태양광 발전량 데이터 셋을 훈련 집합과 평가 집합으로 분할한다. 훈련 집합에서, 각기 다른 은닉층의 개수로 구성된 여러 신경망 모델을 구성하고, 모델별로 Optuna를 적용하여 최적의 초매개변숫값을 선정한다. 다음으로, 은닉층별 최적화된 신경망 모델을 이용해 훈련과 평가 집합에서는 각각 5겹 교차검증을 적용한 발전량 추정값과 예측값을 출력한다. 마지막으로, 스태킹 앙상블 방식을 채택해 기본 초매개변숫값으로 설정해도 우수한 성능을 도출하는 랜덤 포레스트를 이용하여 추정값을 학습하고, 평가 집합의 예측값을 입력으로 받아 최종 태양광 발전량을 예측한다. 인천 지역으로 실험한 결과, 제안한 방식은 모델링이 간편할 뿐만 아니라 여러 신경망 모델보다 우수한 예측 성능을 도출하였으며, 이를 바탕으로 국내 에너지 산업에 이바지할 수 있을 것으로 기대한다.

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2227-2235
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• 2018

Under the current policies and compensation rules in South Korea, Photovoltaic (PV) generation supplier can maximize the profit by combining PV generation with Energy Storage System (ESS). However, the existing operational strategy of ESS is not able to maximize the profit due to the limitation of ESS capacity. In this paper, new ESS scheduling algorithm is introduced by utilizing the System Marginal Price (SMP) and PV generation forecasting to maximize the profits of PV generation supplier. The proposed algorithm determines the charging time of ESS by ranking the charging schedule from low to high SMP when PV generation is more than enough to charge ESS. The discharging time of ESS is determined by ranking the discharging schedule from high to low SMP when ESS energy is not enough to maintain the discharging. To compensate forecasting error, the algorithm is updated every hour to apply the up-to-date information. The simulation is performed to verify the effectiveness of the proposed algorithm by using actual PV generation and ESS information.

• Journal of Electrical Engineering and Technology
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• 제13권4호
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• pp.1504-1514
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• 2018

The objective of this study is to compare the suitability of a non-parametric and 3 parametric distributions in the characterization of prediction intervals of photovoltaic power forecasts with high confidence levels. The prediction intervals of the forecasts are calculated using a method based on recent past data similar to the target forecast input data, and on a distribution assumption for the forecast error. To compare the suitability of the distributions, prediction intervals were calculated using the proposed method and each of the 4 distributions. The calculations were done for one year of day-ahead forecasts of hourly power generation of 432 PV systems. The systems have different sizes and specifications, and are installed in different locations in Japan. The results show that, in general, the non-parametric distribution assumption for the forecast error yielded the best prediction intervals. For example, with a confidence level of 85% the use of the non-parametric distribution assumption yielded a median annual forecast error coverage of 86.9%. This result was close to the one obtained with the Laplacian distribution assumption (87.8% of coverage for the same confidence level). Contrasting with that, using a Gaussian and Hyperbolic distributions yielded median annual forecast error coverage of 89.5% and 90.5%.

• 한국태양에너지학회 논문집
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• 제25권2호
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• pp.9-17
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• 2005

Images taken by geostationary satellite may be used to estimate solar irradiance fluxes at earth's surface. It is based on the empirical correlation between a satellite derived cloud index and the irradiance at the ground. For the validation, estimated solar radiation fluxes are compared with observed solar radiation fluxes at 16 sites over the Korean peninsular from January 1982 to December 2004. Estimated solar radiation fluxes show reliable results for estimating the global radiation with average deviation of -7.8 to +7.0% from the measured values and the yearly averaged horizontal global insolation of Korean peninsula was turned out to be $3.56kW/m^{2}/day$.

• 한국멀티미디어학회논문지
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• 제25권11호
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• pp.1572-1581
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• 2022

Energy conversion to renewable energy is being promoted to solve the recently serious environmental pollution problem. Solar energy is one of the promising natural renewable energy sources. Compared to other energy sources, it is receiving great attention because it has less ecological impact and is sustainable. It is important to predict power generation at a future time in order to maximize the output of solar energy and ensure the stability and variability of power. In this paper, solar power generation data and sensor data were used. Using the PCC(Pearson Correlation Coefficient) analysis method, factors with a large correlation with power generation were derived and applied to the GAM(Generalized Additive Model). And the prediction accuracy of the power generation prediction model was judged. It aims to derive efficient solar power generation in the future and improve power generation performance.