• 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.

• 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.

• 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.

• 한국항행학회논문지
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• 제22권3호
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• pp.233-239
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• 2018

태양광 발전은 기상 상태에 따라 간헐적이기 때문에 태양광 발전의 효율과 경제성 향상을 위해 정확한 발전량 예측이 요구된다. 본 연구는 목포 기상대에서 예보하는 기상 데이터와 영암 태양광 발전소의 발전량 데이터를 이용하여 태양광 발전량 단기 딥러닝 예측모델을 제안하였다. 기상청은 기온, 강수량, 풍향, 풍속, 습도, 운량 등의 기상요소를 3일간 예보한다. 그러나 태양광 발전량 예측에 가장 중요한 기상요소인 일조 및 일사 일사량 예보하지 않는다. 제안 모델은 예보 기상요소를 이용하여, 일조 및 일사 일사량을 예측 하였다. 또한 발전량은 기상요소에 예측된 일조 및 일사 기상요소를 추가하여 예측하였다. 제안 모델의 발전량 예측 결과 DNN의 평균 RMSE와 MAE는 0.177과 0.095이며, RNN은 0.116과 0.067이다. 또한, LSTM은 가장 좋은 결과인 0.100과 0.054이다. 향후 본 연구는 다양한 입력요소의 결합으로 보다 향상된 예측결과를 도출할 수 있을 것으로 기대된다.

• 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.

• Communications for Statistical Applications and Methods
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• 제29권6호
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• pp.709-719
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• 2022

Recently, as the importance of environmental protection has emerged, interest in new and renewable energy is also increasing worldwide. In particular, the solar energy sector accounts for the highest production rate among new and renewable energy in Korea due to its infinite resources, easy installation and maintenance, and eco-friendly characteristics such as low noise emission levels and less pollutants during power generation. However, although climate prediction is essential since solar power is affected by weather and climate change, solar radiation, which is closely related to solar power, is not currently forecasted by the Korea Meteorological Administration. Solar radiation prediction can be the basis for establishing a reasonable new and renewable energy operation plan, and it is very important because it can be used not only in solar power but also in other fields such as power consumption prediction. Therefore, this study was conducted for the purpose of improving the accuracy of solar radiation. Solar radiation was predicted by a total of three weather variables, temperature, humidity, and cloudiness, and solar radiation outside the atmosphere, and the results were compared using various models. The CatBoost model was best obtained by fitting and comparing the Boosting series (XGB, CatBoost) and RNN series (Simple RNN, LSTM, GRU) models. In addition, the results were further improved through Time series cross-validation.

• 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 $3.56kW/m^{2}/day$.

• 태양에너지
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• 제20권3호
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• pp.21-30
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• 2000

It is necessary to estimate the regression coefficients in order to predict the monthly mean daily global radiation on a horizontal surface. Therefore many different equations have proposed to evaluate them for certain areas. In this work, a new correlation has been made to predict the solar radiation for any area over Korea by estimating the regression coefficients taking into account percentage of possible sunshine, and cloud cover. Particularly, the multiple linear regression model proposed shows reliable results for estimating the global radiation with average deviation of -1 to 3 % from the measured values.

• 한국항행학회논문지
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• 제22권5호
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• pp.478-484
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• 2018

본 연구는 기온, 강수량, 풍향, 풍속, 습도, 운량, 일조, 일사 등 시간별 기상 데이터를 이용하여, 일사 및 일조 그리고 태양광 발전예측을 하였다. 지도학습에서 입출력패턴은 예측에서 가장 중요한 요소이지만 인간이 직접 결정해야하기 때문에, 반복적인 실험에 의해 결정해야 한다. 본 연구는 일사 및 일조 예측을 위하여 4가지 모델의 입출력 패턴을 제안하였다. 또한, 예측된 일조 및 일사 데이터와 전라남도 영암 태양광 발전소의 발전량 데이터를 사용하여 태양광 발전량을 예측하였다. 실험결과 일조 및 일사 예측에서 모델 4가 가장 예측결과가 우수했으며, 모델 1에 비해 일조의 RMSE는 1.5배 정도 그리고 일사의 RMSE는 3배 정도 오차가 줄었다. 태양광 발전예측 실험결과 일조 및 일사와 마찬가지로 모델 4가 가장 예측결과가 좋았으며, 모델 1 보다 RMSE가 2.7배 정도 오차가 줄었다.