scholarly journals Nowcasting of Surface Solar Irradiance Using FengYun-4 Satellite Observations over China

2019 ◽  
Vol 11 (17) ◽  
pp. 1984 ◽  
Author(s):  
Yang ◽  
Gao ◽  
Li ◽  
Jia ◽  
Jiang
Keyword(s):  
Solar Irradiance ◽  
Satellite Images ◽  
Motion Vector ◽  
Northwest China ◽  
Mean Bias Error ◽  
Bias Error ◽  
Random Mutation ◽  
Time Horizons ◽  
Desert Areas ◽  
Sky Conditions

The accurate prediction of surface solar irradiance is of great significance for the generation of photovoltaic power. Surface solar irradiance is affected by many random mutation factors, which means that there are great challenges faced in short-term prediction. In Northwest China, there are abundant solar energy resources and large desert areas, which have broad prospects for the development of photovoltaic (PV) systems. For the desert areas in Northwest China, where meteorological stations are scarce, satellite remote sensing data are extremely precious exploration data. In this paper, we present a model using FY-4A satellite images to forecast (up to 15–180 min ahead) global horizontal solar irradiance (GHI), at a 15 min temporal resolution in desert areas under different sky conditions, and compare it with the persistence model (SP). The spatial resolution of the FY-4A satellite images we used was 1 km × 1 km. Particle image velocimetry (PIV) was used to derive the cloud motion vector (CMV) field from the satellite cloud images. The accuracy of the forecast model was evaluated by the ground observed GHI data. The results showed that the normalized root mean square error (nRMSE) ranged from 18.9% to 21.6% and the normalized mean bias error (nMBE) ranged from 3.2% to 4.9% for time horizons from 15 to 180 min under all sky conditions. Compared with the SP model, the nRMSE value was reduced by about 6%, 8%, and 14% with the time horizons of 60, 120, and 180 min, respectively.

scholarly journals Application of Semi-Empirical Models Based on Satellite Images for Estimating Solar Irradiance in Korea

2021 ◽  
Vol 11 (8) ◽  
pp. 3445
Author(s):  
Pranda M. P. Garniwa ◽  
Raden A. A. Ramadhan ◽  
Hyun-Jin Lee
Keyword(s):  
Hybrid Model ◽  
Solar Irradiance ◽  
Satellite Images ◽  
Short Term Memory ◽  
Empirical Models ◽  
Mean Bias Error ◽  
Bias Error ◽  
Intermittent Behavior ◽  
Electricity Grids ◽  
Semi Empirical

The application of solar energy as a renewable energy source has significantly escalated owing to its abundance and availability worldwide. However, the intermittent behavior of solar irradiance is a serious disadvantage for electricity grids using photovoltaic (PV) systems. Thus, reliable solar irradiance data are vital to achieve consistent energy production. Geostationary satellite images have become a solution to this issue, as they represent a database for solar irradiance on a massive spatiotemporal scale. The estimation of global horizontal irradiance (GHI) using satellite images has been developed based on physical and semi-empirical models, but only a few studies have been dedicated to modeling GHI using semi-empirical models in Korea. Therefore, this study conducted a comparative analysis to determine the most suitable semi-empirical model of GHI in Korea. Considering their applicability, the Beyer, Rigollier, Hammer, and Perez, models were selected to estimate the GHI over Seoul, Korea. After a comparative evaluation, the Hammer model was determined to be the best model. This study also introduced a hybrid model and applied a long short-term memory (LSTM) model in order to improve prediction accuracy. The hybrid model exhibited a smaller root-mean-square error (RMSE), 97.08 W/m2, than that of the Hammer model, 103.92 W/m2, while producing a comparable mean-bias error. Meanwhile, the LSTM model showed the potential to further reduce the RMSE by 11.2%, compared to the hybrid model.

scholarly journals 3D-VAR Data Assimilation of SEVIRI Radiances for the Prediction of Solar Irradiance in Italy Using WRF Solar Mesoscale Model—Preliminary Results

2020 ◽  
Vol 12 (6) ◽  
pp. 920 ◽  
Author(s):  
Sabrina Gentile ◽  
Francesco Di Paola ◽  
Domenico Cimini ◽  
Donatello Gallucci ◽  
Edoardo Geraldi ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Solar Irradiance ◽  
Cloud Cover ◽  
Weather Prediction ◽  
National Research Council ◽  
Mean Bias Error ◽  
Bias Error ◽  
Clear Sky ◽  
Sky Conditions ◽  
The Impact

Solar power generation is highly fluctuating due to its dependence on atmospheric conditions. The integration of this variable resource into the energy supply system requires reliable predictions of the expected power production as a basis for management and operation strategies. This is one of the goals of the Solar Cloud project, funded by the Italian Ministry of Economic Development (MISE)—to provide detailed forecasts of solar irradiance variables to operators and organizations operating in the solar energy industry. The Institute of Methodologies for Environmental Analysis of the National Research Council (IMAA-CNR), participating to the project, implemented an operational chain that provides forecasts of all the solar irradiance variables at high temporal and horizontal resolution using the numerical weather prediction Advanced Research Weather Research and Forecasting (WRF-ARW) Solar version 3.8.1 released by the National Center for Atmospheric Research (NCAR) in August 2016. With the aim of improving the forecast of solar irradiance, the three-dimensional (3D-Var) data assimilation was tested to assimilate radiances from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) geostationary satellite into WRF Solar. To quantify the impact, the model output is compared against observational data. Hourly Global Horizontal Irradiance (GHI) is compared with ground-based observations from Regional Agency for the Protection of the Environment (ARPA) and with MSG Shortwave Solar Irradiance estimations, while WRF Solar cloud coverage is compared with Cloud Mask by MSG. A preliminary test has been performed in clear sky conditions to assess the capability of the model to reproduce the diurnal cycle of the solar irradiance. The statistical scores for clear sky conditions show a positive performance of the model with values comparable to the instrument uncertainty and a correlation of 0.995. For cloudy sky, the solar irradiance and the cloud cover are better simulated when the SEVIRI radiances are assimilated, especially in the short range of the simulation. For the cloud cover, the Mean Bias Error one hour after the assimilation time is reduced from 41.62 to 20.29 W/m2 when the assimilation is activated. Although only two case studies are considered here, the results indicate that the assimilation of SEVIRI radiance improves the performance of WRF Solar especially in the first 3 hour forecast.

scholarly journals Adaptation of an empirical model for erythemal ultraviolet irradiance

2007 ◽  
Vol 25 (7) ◽  
pp. 1499-1508 ◽  
Author(s):  
I. Foyo-Moreno ◽  
I. Alados ◽  
L. Alados-Arboledas
Keyword(s):  
Empirical Model ◽  
Independent Set ◽  
Mean Bias Error ◽  
Bias Error ◽  
Data Set ◽  
Uv Index ◽  
Ultraviolet Irradiance ◽  
Sky Conditions ◽  
Mean Square Errors ◽  
Radiation Measurements

Abstract. In this work we adapt an empirical model to estimate ultraviolet erythemal irradiance (UVER) using experimental measurements carried out at seven stations in Spain during four years (2000–2003). The measurements were taken in the framework of the Spanish UVB radiometric network operated and maintained by the Spanish Meteorological Institute. The UVER observations are recorded as half hour average values. The model is valid for all-sky conditions, estimating UVER from the ozone columnar content and parameters usually registered in radiometric networks, such as global broadband hemispherical transmittance and optical air mass. One data set was used to develop the model and another independent set was used to validate it. The model provides satisfactory results, with low mean bias error (MBE) for all stations. In fact, MBEs are less than 4% and root mean square errors (RMSE) are below 18% (except for one location). The model has also been evaluated to estimate the UV index. The percentage of cases with differences of 0 UVI units is in the range of 61.1% to 72.0%, while the percentage of cases with differences of ±1 UVI unit covers the range of 95.6% to 99.2%. This result confirms the applicability of the model to estimate UVER irradiance and the UV index at those locations in the Iberian Peninsula where there are no UV radiation measurements.

scholarly journals Using digital image processing to characterize the Campbell–Stokes sunshine recorder and to derive high-temporal resolution direct solar irradiance

2015 ◽  
Vol 8 (1) ◽  
pp. 183-194 ◽  
Author(s):  
A. Sanchez-Romero ◽  
J. A. González ◽  
J. Calbó ◽  
A. Sanchez-Lorenzo
Keyword(s):  
Image Processing ◽  
Temporal Resolution ◽  
Solar Irradiance ◽  
Atmospheric Aerosol ◽  
Mean Squared Error ◽  
Sunshine Duration ◽  
Strong Relationship ◽  
Mean Bias Error ◽  
Bias Error ◽  
High Temporal Resolution

Abstract. The Campbell–Stokes sunshine recorder (CSSR) has been one of the most commonly used instruments for measuring sunshine duration (SD) through the burn length of a given CSSR card. Many authors have used SD to obtain information about cloudiness and solar radiation (by using Ångström–Prescott type formulas), but the burn width has not been used systematically. In principle, the burn width increases for increasing direct beam irradiance. The aim of this research is to show the relationship between burn width and direct solar irradiance (DSI) and to prove whether this relationship depends on the type of CSSR and burning card. A method of analysis based on image processing of digital scanned images of burned cards is used. With this method, the temporal evolution of the burn width with 1 min resolution can be obtained. From this, SD is easily calculated and compared with the traditional (i.e., visual) determination. The method tends to slightly overestimate SD, but the thresholds that are used in the image processing could be adjusted to obtain an improved estimation. Regarding the burn width, experimental results show that there is a high correlation between two different models of CSSRs, as well as a strong relationship between burn widths and DSI at a high-temporal resolution. Thus, for example, hourly DSI may be estimated from the burn width with higher accuracy than based on burn length (for one of the CSSR, relative root mean squared error is 24 and 30%, respectively; mean bias error is −0.6 and −30.0 W m−2, respectively). The method offers a practical way to exploit long-term sets of CSSR cards to create long time series of DSI. Since DSI is affected by atmospheric aerosol content, CSSR records may also become a proxy measurement for turbidity and atmospheric aerosol loading.

scholarly journals Using digital image processing to characterize the Campbell–Stokes sunshine recorder and to derive high-temporal resolution direct solar irradiance

2014 ◽  
Vol 7 (9) ◽  
pp. 9537-9571
Author(s):  
A. Sanchez-Romero ◽  
J. A. González ◽  
J. Calbó ◽  
A. Sanchez-Lorenzo
Keyword(s):  
Image Processing ◽  
Temporal Resolution ◽  
Solar Irradiance ◽  
Atmospheric Aerosol ◽  
Mean Squared Error ◽  
Sunshine Duration ◽  
Mean Bias Error ◽  
Unbiased Estimation ◽  
Bias Error ◽  
High Temporal Resolution

Abstract. The Campbell–Stokes sunshine recorder (CSSR) has been one of the most commonly used instruments for measuring sunshine duration (SD) through the burn length of a given CSSR card. Many authors have used SD to obtain information about cloudiness and solar radiation (by using Ångström–Prescott type formulas). Contrarily, the burn width has not been used systematically. In principle, the burn width increases for increasing direct beam irradiance. The aim of this research is to show the relationship between burn width and direct solar irradiance (DSI), and to prove whether this relationship depends on the type of CSSR and burning card. A semi-automatic method based on image processing of digital scanned images of burnt cards is presented. With this method, the temporal evolution of the burn width with 1 min resolution can be obtained. From this, SD is easily calculated and compared with the traditional (i.e. visual) determination. The method tends to slightly overestimate SD but the thresholds that are used in the image processing could be adjusted to obtain an unbiased estimation. Regarding the burn width, results show that there is a high correlation between two different models of CSSRs, as well as a strong relationship between burn widths and DSI at a high-temporal resolution. Thus, for example, hourly DSI may be estimated from the burn width with higher accuracy than based on burn length (for one of the CSSR, relative root mean squared error 24 and 30% respectively; mean bias error −0.6 and −30.0 W m−2 respectively). The method offers a practical way to exploit long-term sets of CSSR cards to create long time series of DSI. Since DSI is affected by atmospheric aerosol content, CSSR records may also become a proxy measurement for turbidity and atmospheric aerosol loading.

scholarly journals A Novel Hybrid Model of WRF and Clearness Index-Based Kalman Filter for Day-Ahead Solar Radiation Forecasting

2019 ◽  
Vol 9 (19) ◽  
pp. 3967 ◽  
Author(s):  
Yuzhang Che ◽  
Lewei Chen ◽  
Jiafeng Zheng ◽  
Liang Yuan ◽  
Feng Xiao
Keyword(s):  
Kalman Filter ◽  
Solar Radiation ◽  
Hybrid Model ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Energy Utilization ◽  
Mean Bias Error ◽  
Bias Error ◽  
Clearness Index ◽  
Sky Conditions

Day-ahead forecasting of solar radiation is essential for grid balancing, real-time unit dispatching, scheduling and trading in the solar energy utilization system. In order to provide reliable forecasts of solar radiation, a novel hybrid model is proposed in this study. The hybrid model consists of two modules: a mesoscale numerical weather prediction model (WRF: Weather Research and Forecasting) and Kalman filter. However, the Kalman filter is less likely to predict sudden changes in the forecasting errors. To address this shortcoming, we develop a new framework to implement a Kalman filter based on the clearness index. The performance of this hybrid model is evaluated using a one-year dataset of solar radiation taken from a photovoltaic plant located at Maizuru, Japan and Qinghai, China, respectively. The numerical results reveal that the proposed hybrid model performs much better in comparison with the WRF-alone forecasts under different sky conditions. In particular, in the case of clear sky conditions, the hybrid model can improve the forecasting accuracy by 95.7% and 90.9% in mean bias error (MBE), and 42.2% and 26.8% in root mean square error (RMSE) for Maizuru and Qinghai sites, respectively.

scholarly journals Estimation of Actual Evapotranspiration Using the Remote Sensing Method and SEBAL Algorithm: A Case Study in Ein Khosh Plain, Iran

Hydrology ◽  
2020 ◽  
Vol 7 (2) ◽  
pp. 36
Author(s):  
Amir Ghaderi ◽  
Mehdi Dasineh ◽  
Maryam Shokri ◽  
John Abraham
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Surface Energy Balance ◽  
Sufficient Accuracy ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Landsat 8 ◽  
Irrigation Requirements

The aim of this study was to estimate evapotranspiration (ET) using remote sensing and the Surface Energy Balance Algorithm for Land (SEBAL) in the Ilam province, Iran. Landsat 8 satellite images were used to calculate ET during the cultivation and harvesting of wheat crops. The evaluation using SEBAL, along with the FAO-Penman–Monteith method, showed that SEBAL has a sufficient accuracy for estimating ET. The values of the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Bias Error (MBE), and correlation coefficient were 0.466, 2.9%, 0.222 mm/day, and 0.97, respectively. Satellite images showed that rainfall, except for the last month of cultivation, provided the necessary water requirements and there was no requirement for the use of other water resources for irrigation, with the exception of late May and early June. The maximum ET on the Ein Khosh Plain occurred in March. The irrigation requirements showed that the Ein Khosh Plain in March, which witnessed the highest ET, did not experience any deficiency of rainfall that month. However, during April and May, with maxima of 50 and 70 mm, respectively, water was needed for irrigation. During the plant growth periods, the greatest and least amount of water required were 231.23 and 19.47 mm/hr, respectively.

scholarly journals Regression Model to Predict Global Solar Irradiance in Malaysia

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Hairuniza Ahmed Kutty ◽  
Muhammad Hazim Masral ◽  
Parvathy Rajendran
Keyword(s):  
Regression Model ◽  
Solar Irradiance ◽  
Cloud Cover ◽  
Prediction Models ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Proposed Model ◽  
Multiple Parameter ◽  
Gust Speed

A novel regression model is developed to estimate the monthly global solar irradiance in Malaysia. The model is developed based on different available meteorological parameters, including temperature, cloud cover, rain precipitate, relative humidity, wind speed, pressure, and gust speed, by implementing regression analysis. This paper reports on the details of the analysis of the effect of each prediction parameter to identify the parameters that are relevant to estimating global solar irradiance. In addition, the proposed model is compared in terms of the root mean square error (RMSE), mean bias error (MBE), and the coefficient of determination (R2) with other models available from literature studies. Seven models based on single parameters (PM1 to PM7) and five multiple-parameter models (PM7 to PM12) are proposed. The new models perform well, with RMSE ranging from 0.429% to 1.774%,R2ranging from 0.942 to 0.992, and MBE ranging from −0.1571% to 0.6025%. In general, cloud cover significantly affects the estimation of global solar irradiance. However, cloud cover in Malaysia lacks sufficient influence when included into multiple-parameter models although it performs fairly well in single-parameter prediction models.

scholarly journals Comparison of hourly surface downwelling solar radiation estimated from MSG/SEVIRI and forecast by RAMS model with pyranometers over Italy

2017 ◽  
Author(s):  
Stefano Federico ◽  
Rosa Claudia Torcasio ◽  
Paolo Sanò ◽  
Daniele Casella ◽  
Monica Campanelli ◽  
...  
Keyword(s):  
Mesoscale Model ◽  
Horizontal Resolution ◽  
Climatic Conditions ◽  
Atmospheric Modeling ◽  
Mean Bias Error ◽  
Bias Error ◽  
Hourly Data ◽  
Sky Conditions ◽  
The Impact ◽  
Rams Model

Abstract. In this paper, we evaluate the performance of two Global Horizontal solar Irradiance (GHI) estimates, one derived from Meteosat Second Generation (MSG) and another from one-day forecast of the Regional Atmospheric Modeling System (RAMS) mesoscale model. The horizontal resolution of the MSG-GHI is 3*5 km2 over Italy, which is the focus area of this study. For this paper, RAMS has the horizontal resolution of 4 km. The performance of MSG-GHI estimate and RAMS-GHI one-day forecast are evaluated for one year (1 June 2013–31 May 2014) against data of twelve ground based pyranometers over Italy spanning a range of climatic conditions, i.e. from maritime Mediterranean to Alpine climate. Statistics on hourly GHI and daily integrated GHI are presented for the four seasons and the whole year for all the measurement sites. Different sky conditions are considered in the analysis. Results on hourly data show an evident dependence on the sky conditions, with the Root Mean Square Error (RMSE) increasing from clear to contaminated, and to overcast conditions. The RMSE increases substantially for Alpine stations in all the seasons, mainly because of the increase of the cloud coverage for these stations, which is not well represented at the satellite and model resolutions. Considering the yearly statistics for the RAMS model, the RMSE ranges from 152 W/m2 (31 %) obtained for Cozzo Spadaro, a maritime station, to 287 W/m2 (82 %) for Aosta, an Alpine site. Considering the yearly statistics for MSG-GHI, the minimum RMSE is for Cozzo Spadaro (71 W/m2 , 14 %), while the maximum is for Aosta (181 W/m2 , 51 %). The Mean Bias Error (MBE) shows the tendency of RAMS to over forecast the GHI, while no specific tendency if found for MSG-GHI. Results for daily integrated GHI show a reduction of the RMSE of at least 10 %, compared to hourly GHI evaluation, for both RAMS-GHI one-day forecast and MSG-GHI estimate. A partial compensation of underestimation and overestimation of the GHI contributes to the RMSE reduction. Furthermore, a post-processing technique, namely Model Output Statistics (MOS), is applied to hourly and daily integrated GHI. The application of MOS shows an improvement for RAMS-GHI up to 24 %, depending on the site considered, while the impact of MOS on MSG-GHI RMSE is small (2–3 %).

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