• 한국산업융합학회 논문집
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• 제26권4_2호
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• pp.715-724
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• 2023

Solar energy forecasting is essential for (1) power system planning, management, and operation, requiring accurate predictions. It is crucial for (2) ensuring a continuous and sustainable power supply to customers and (3) optimizing the operation and control of renewable energy systems and the electricity market. Recently, research has been focusing on developing solar energy forecasting models that can provide daily plans for power usage and production and be verified in the electricity market. In these prediction models, various data, including solar energy generation and climate data, are chosen to be utilized in the forecasting process. The most commonly used climate data (such as temperature, relative humidity, precipitation, solar radiation, and wind speed) significantly influence the fluctuations in solar energy generation based on weather conditions. Therefore, this paper proposes a hybrid forecasting model by combining the strengths of the Prophet model and the GRU model, which exhibits excellent predictive performance. The forecasting periods for solar energy generation are tested in short-term (2 days, 7 days) and medium-term (15 days, 30 days) scenarios. The experimental results demonstrate that the proposed approach outperforms the conventional Prophet model by more than twice in terms of Root Mean Square Error (RMSE) and surpasses the modified GRU model by more than 1.5 times, showcasing superior performance.

• 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.

• 한국태양에너지학회 논문집
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• 제39권4호
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• pp.41-54
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• 2019

The proportion of solar photovoltaic power generation has steadily increased in the power trade market. Solar energy forecast is highly important for the stable trade of volatile solar energy in the existing power trade market, and it is necessary to identify accurately any forecast error according to the forecast lead time. This paper analyzes the latest study trend in solar energy forecast overseas and presents a consistent comparative assessment by adopting a single statistical variable (nRMSE) for forecast errors according to lead time and forecast technology.

• 한국태양에너지학회 논문집
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• 제26권2호
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• pp.27-34
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• 2006

In this paper, a forecasting model of wind speed at Walryong Site, Jeju Island is presented, which has been developed and evaluated as a first step toward establishing Korea Forecasting Model of Wind Power Generation. The forecasting model is constructed based on neural network and is trained with wind speed data observed at Cosan Weather Station located near by Walryong Site. Due to short period of measurements at Walryong Site for training statistical model Gosan Weather Station's long-term data are substituted and then transplanted to Walryong Site by using Measure-Correlate-Predict technique. One to three-hour advance forecasting of wind speed show good agreements with the monitoring data of Walryong site with the correlation factors 0.96 and 0.88, respectively.

• Advances in Energy Research
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• 제6권2호
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• pp.131-143
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• 2019

Global Solar Radiation (GSR) is the key element for performance estimation of any Solar Power Plant (SPP). Its forecasting may help in estimation of power production from a SPP well in advance, and may also render help in optimal use of this power. Seasonal Auto-Regressive Moving Average (SARMA) and Artificial Neural Network (ANN) models are combined in order to develop a hybrid model (SARMA-ANN) conceiving the characteristics of both linear and non-linear prediction models. This developed model has been used for prediction of GSR at Gorakhpur, situated in the northern region of India. The proposed model is beneficial for the univariate forecasting. Along with this model, we have also used Auto-Regressive Moving Average (ARMA), SARMA, ANN based models for 1 - 6 day-ahead forecasting of GSR on hourly basis. It has been found that the proposed model presents least RMSE (Root Mean Square Error) and produces best forecasting results among all the models considered in the present study. As an application, the comparison between the forecasted one and the energy produced by the grid connected PV plant installed on the parking stands of the University shows the superiority of the proposed model.

• 응용통계연구
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• 제31권6호
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• pp.785-799
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• 2018

신재생에너지 산업이 발전함에 따라 태양광 발전에 대한 중요성이 확대되고 있다. 태양광 발전량을 정확히 예측하기 위해서는 일사량 예측이 필수적이다. 본 논문에서는 태양광 패널이 존재하는 청주와 광주 지역을 선정하여 기상포털에서 제공하는 시간별 기상 데이터를 수집하여 연구하였다. 일사량 예측을 위하여 시계열 모형인 ARIMA, ARIMAX, seasonal ARIMA, seasonal ARIMAX, ARIMA-GARCH, ARIMAX-GARCH, seasonal ARIMA-GARCH, seasonal ARIMAX-GARCH 모형을 비교하였다. 본 연구에서는 모형의 예측 성능을 비교하고자 mean absolute error와 root mean square error를 사용하였다. 모형들의 예측 성능 비교 결과 일사량만 고려하였을 때는 이분산 문제를 고려한 seasonal ARIMA-GARCH 모형이 우수한 성능을 나타냈고, 외생변수를 활용한 ARIMAX 모형으로 일사량 예측을 한 경우가 가장 좋은 예측력을 나타냈다.

• 한국태양에너지학회 논문집
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• 제30권2호
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• pp.46-53
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• 2010

In this paper the feasibility of wind power forecasting from MOS(Model Output Statistics) was evaluated at Gosan area in Jeju during February to Octoberin 2008. The observed wind data from wind turbine was compared with 24 hours and 48 hours forecasting wind data from MOS predicting. Coefficient of determination of measured wind speed from wind turbine and 24 hours forecasting from MOS was around 0.53 and 48 hours was around 0.30. These determination factors were increased to 0.65 from 0.53 and 0.35 from 0.30, respectively, when it comes to the prevailing wind direction($300^{\circ}\sim60^{\circ}$). Wind power forecasting ratio in 24 hours of MOS showed a value of 0.81 within 70% confidence interval and it also showed 0.65 in 80% confidence interval. It is suggested that the additional study of weather conditions be carried out when large error happened in MOS forecasting.

• 한국환경과학회지
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• 제26권10호
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• pp.1167-1180
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• 2017

Numerical Weather Prediction (NWP) models such as the Weather Research and Forecasting (WRF) model are essential for forecasting one-day-ahead solar irradiance. In order to evaluate the performance of the WRF in forecasting solar irradiance over the Korean Peninsula, we compared WRF prediction data from 2008 to 2010 corresponding to weather observation data (OBS) from the Korean Meteorological Administration (KMA). The WRF model showed poor performance at polluted regions such as Seoul and Suwon where the relative Root Mean Square Error (rRMSE) is over 30%. Predictions by the WRF model alone had a large amount of potential error because of the lack of actual aerosol radiative feedbacks. For the purpose of reducing this error induced by atmospheric particles, i.e., aerosols, the WRF model was coupled with the Community Multiscale Air Quality (CMAQ) model. The coupled system makes it possible to estimate the radiative feedbacks of aerosols on the solar irradiance. As a result, the solar irradiance estimated by the coupled system showed a strong dependence on both the aerosol spatial distributions and the associated optical properties. In the NF (No Feedback) case, which refers to the WRF-only stimulated system without aerosol feedbacks, the GHI was overestimated by $50-200W\;m^{-2}$ compared with OBS derived values at each site. In the YF (Yes Feedback) case, in contrast, which refers to the WRF-CMAQ two-way coupled system, the rRMSE was significantly improved by 3.1-3.7% at Suwon and Seoul where the Particulate Matter (PM) concentrations, specifically, those related to the $PM_{10}$ size fraction, were over $100{\mu}g\;m^{-3}$. Thus, the coupled system showed promise for acquiring more accurate solar irradiance forecasts.

• 천문학논총
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• 제13권1호
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• pp.39-54
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• 1998

We have developed solar observational system in the department of Astronomy & Space Sciences of KyungHee University, in order to monitor solar activities and construct solar database for space weather forecasting at maximum of 23rd solar cycle, as well as an solar education and exercise for undergraduate students. Our solar observational system consists of the full disk monitoring system and the regional observation system for H a fine structure. Full disk monitoring system is made of an energy rejection filter, 16cm refractor, video CCD camera and monitor. Monitored data are recorded to VHS video tape and analog output of video CCD can be captured as digital images by the computer with video graphic card. Another system for regional observation of the sun is made of energy rejection filter, 21cm Schmidt-Cassegrain reflector, H a filter with 1.6A pass band width and $375\times242$ CCD camera. We can observe H a fine structure in active regions of solar disk and solar limb, by using this system. We have carried out intense solar observations for a test of our system. It is found that Quality of our H a image is as good as that of solar images provided by Space Environmental Center. In this paper, we introduce the basic characteristics of the KyungHee Solar Observation System and result of our solar observations. We hope that our data should be used for space weather forecasting with domestic data of RRL(Radio Research Laboratory) and SOFT(SOlar Flare Telescope).

• 신재생에너지
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• 제19권4호
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• pp.98-107
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• 2023

A study was conducted to analyze data from 1981 to 2020 for understanding the impact of climate on solar energy generation. A significant increase of 104.6 kWhm^-2 was observed in the annual cumulative solar radiation over this period. Notably, the distribution of solar radiation shifted, with the solar radiation in Busan rising from the seventh place in 1981 to the second place in 2020 in South Korea. This study also examined the correlation between long-term temperature trends and solar radiation. Areas with the highest solar radiation in 2020, such as Busan, Gwangju, Daegu, and Jinju, exhibited strong positive correlations, suggesting that increased solar radiation contributed to higher temperatures. Conversely, regions like Seosan and Mokpo showed lower temperature increases due to factors such as reduced cloud cover. To evaluate the impact on solar energy production, simulations were conducted using climate data from both years. The results revealed that relying solely on historical data for solar energy predictions could lead to overestimations in some areas, including Seosan or Jinju, and underestimations in others such as Busan. Hence, considering long-term climate variability is vital for accurate solar energy forecasting and ensuring the economic feasibility of solar projects.