• 설비공학논문집
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• 제25권6호
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• pp.310-316
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• 2013

This study was designed to investigate a method for short-term, real-time energy demand prediction, to cope with changing loads for the effective operation and management of buildings. Through a case study, a novel methodology for real-time energy demand prediction with the use of weather forecasting data was suggested. To perform the input and output operations of weather data, and to calculate solar radiation and EnergyPlus, the BCVTB (Building Control Virtual Test Bed) was designed. Through the BCVTB, energy demand prediction for the next 24 hours was carried out, based on 4 real-time weather data and 2 solar radiation calculations. The weather parameters used in a model equation to calculate solar radiation were sourced from the weather data of the KMA (Korea Meteorological Administration). Depending on the local weather forecast data, the results showed their corresponding predicted values. Thus, this methodology was successfully applicable to anywhere that local weather forecast data is available.

• 천문학회보
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• 제44권1호
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• pp.51.2-51.2
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• 2019

Multi-wavelength observations become very popular in astronomy. Even though there are some correlations among different sensor images, it is not easy to translate from one to the other one. In this study, we apply a deep learning method for image-to-image translation, based on conditional generative adversarial networks (cGANs), to solar images. To examine the validity of the method for scientific data, we consider several different types of pairs: (1) Generation of SDO/EUV images from SDO/HMI magnetograms, (2) Generation of backside magnetograms from STEREO/EUVI images, (3) Generation of EUV & X-ray images from Carrington sunspot drawing, and (4) Generation of solar magnetograms from Ca II images. It is very impressive that AI-generated ones are quite consistent with actual ones. In addition, we apply the convolution neural network to the forecast of solar flares and find that our method is better than the conventional method. Our study also shows that the forecast of solar proton flux profiles using Long and Short Term Memory method is better than the autoregressive method. We will discuss several applications of these methodologies for scientific research.

• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1530-1537
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• 2016

Recently, solar power generation shows the significant growth in the renewable energy field. Using the short-term prediction, it is possible to control the electric power demand and the power generation plan of the auxiliary device. However, a short-term prediction can be used when you know the weather forecast. If it is not possible to use the weather forecast information because of disconnection of network at the island and the mountains or for security reasons, the accuracy of prediction is not good. Therefore, in this paper, we proposed a system capable of short-term prediction of solar power generation amount by using only the weather information that has been collected by oneself. We used temperature, humidity and insolation as weather information. We have applied a moving average to each information because they had a characteristic of time series. It was composed of min, max and average of each information, differences of mutual information and gradient of it. An artificial neural network, SVM and RBF Network model was used for the prediction algorithm and they were combined by Ensemble method. The results of this suggest that using a moving average during pre-processing and ensemble prediction models will maximize prediction accuracy.

• 천문학회보
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• 제34권1호
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• pp.81.2-81.2
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• 2009

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2009년도 한국우주과학회보 제18권1호
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• pp.45.2-45.2
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• 2009

• 한국인터넷방송통신학회논문지
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• 제15권2호
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• pp.129-135
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• 2015

본 논문에서는 가로등 운용에 있어서 일기예보 및 일조량을 고려한 알고리즘을 제안하였다. 이 알고리즘에 의해 생성된 Weather Factor를 적용하여 보행자가 있을 시에는 가로등의 광량을 최대로 유지하고 보행자가 없을 경우 최대전력을 사용하지 않고 일정한 밝기를 유지하는 대기전력모드를 사용하여 전력소비를 줄였다. 이렇게 함으로써 배터리의 잔량을 확보할 수 있으며 이를 이용하여 부조일이 지속될 경우 운용일수를 최대한 연장하기 위한 적절한 알고리즘을 제안하였다. 또한 이러한 알고리즘에 필요한 Weather Factor의 값을 실험을 통하여 결정하였으며. 모의실험을 통해 알고리즘의 적합성을 확인하였다.

• 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.

• 천문학회지
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• 제53권6호
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• pp.139-147
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• 2020

The sunspot area is a critical physical quantity for assessing the solar activity level; forecasts of the sunspot area are of great importance for studies of the solar activity and space weather. We developed an innovative hybrid model prediction method by integrating the complementary ensemble empirical mode decomposition (CEEMD) and extreme learning machine (ELM). The time series is first decomposed into intrinsic mode functions (IMFs) with different frequencies by CEEMD; these IMFs can be divided into three groups, a high-frequency group, a low-frequency group, and a trend group. The ELM forecasting models are established to forecast the three groups separately. The final forecast results are obtained by summing up the forecast values of each group. The proposed hybrid model is applied to the smoothed monthly mean sunspot area archived at NASA's Marshall Space Flight Center (MSFC). We find a mean absolute percentage error (MAPE) and a root mean square error (RMSE) of 1.80% and 9.75, respectively, which indicates that: (1) for the CEEMD-ELM model, the predicted sunspot area is in good agreement with the observed one; (2) the proposed model outperforms previous approaches in terms of prediction accuracy and operational efficiency.

• 한국농림기상학회지
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• 제17권4호
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• pp.281-289
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• 2015

일조시간 및 일사량은 작물생육에 중요한 기상요소이지만 기상청 동네예보 항목에 없기 때문에 3시간 간격 '하늘상태'를 활용하여 일조시간 및 수평면 일사량을 추정하는 방법을 고안하였다. 기상청 동네예보의 3시간 간격 '하늘상태' 자료를 수집하고 전국 22개 일사관측 기상대의 동시간대 실측 운량과 비교하여 '하늘상태'의 4단계 격자값 '맑음(1)', '구름조금(2)', '구름많음(3)', '흐림(4)'을 0부터 10까지의 운량으로 변환하였다. 22개 일사관측 기상대의 일 평균운량 0인 날에 대하여 일조율을 비교하여 관측여건이 가장 좋은 3개 지점을 선정하였다. 선정된 지점의 3년치 운량과 일조시간 실측자료로부터 운량-일조시간 추정식을 도출하였으며, 이 식에 의해 추정된 일조시간값으로 Angstrom-Prescott 모형을 구동하여 수평면 일사량을 산출하였다. '하늘상태' 기반으로 추정된 일조시간 및 일사량을 3 지점에서 2년간 실측자료와 비교한 결과 RMSE 기준 일조시간 추정오차는 1.5~1.7 시간, 일사량 추정오차는 $2.5{\sim}3.0MJ\;m^{-2}\;day^{-1}$ 이었다.

• Current Photovoltaic Research
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• 제4권2호
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• pp.80-86
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• 2016

Since the last five years it has become a lot of solar power plants installed. However, by installing the large-scale solar power station it is not easy to predict the actual generation years. Because there are a variety of factors, such as changes daily solar radiation, temperature and humidity. If the power output can be measured accurately it predicts profits also we can measure efficiency for solar power plants precisely. Therefore, Prediction of power generation is forecast to be a useful research field. In this study, out discovering the factors that can improve the accuracy of the prediction of the photovoltaic power generation presents the means to apply them to the power generation amount prediction.