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 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 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 Retrieving Water Turbidity in Araucanian Lakes (South-Central Chile) Based on Multispectral Landsat Imagery

2021 ◽  
Vol 13 (16) ◽  
pp. 3133
Author(s):  
Lien Rodríguez-López ◽  
Iongel Duran-Llacer ◽  
Lisdelys González-Rodríguez ◽  
Rolando Cardenas ◽  
Roberto Urrutia
Keyword(s):  
Landsat Imagery ◽  
Water Clarity ◽  
Empirical Models ◽  
Mean Bias Error ◽  
Bias Error ◽  
Landsat Images ◽  
Central Chile ◽  
Temporal Representation ◽  
South Central ◽  
Combination Bands

Remote sensing was used as an early alert tool for water clarity changes in five Araucanian Lakes in South-Central Chile. Turbidity records are scarce or unavailable over large and remote areas needed to fully understand the factors associated with turbidity, and their spatial-temporal representation remains a limitation. This work aimed to develop and validate empirical models to estimate values of turbidity from Landsat images and determine the spatial distribution of estimated turbidity in the selected Araucanian Lakes. Secchi disk depth measurements were linked with turbidity measurements to obtain a turbidity dataset. This in turn was used to develop and validate a set of empirical models to predict turbidity based on four single bands and 16 combination bands from 15 multispectral Landsat images. The best empirical models predicted turbidity over the range of 0.3–12.3 NTUs with RMSE values around 0.31–1.03 NTU, R2 (Index of Agreement IA) around 0.93–0.99 (0.85–0.97) and mean bias error (MBE) around (−0.36–0.44 NTU). Estimation maps to analyze the temporal-spatial turbidity variation in the lakes were constructed. Finally, it was found that the meteorological conditions may affect the variation of turbidity, mainly precipitation and wind speed. The data indicate that the turbidity has slightly increased in winter–spring. These models will be used in the future to reconstruct large datasets that allow analyzing transparency trends in those lakes.

scholarly journals Reference crop evapotranspiration in distinct agricultural regions of Southern Brazil: a comparison of improved empirical models

2021 ◽  
Vol 29 ◽  
Author(s):  
Maicon Sérgio Nascimento dos Santos ◽  
Isac Aires de Castro ◽  
Carolina Elisa Demaman Oro ◽  
Giovani Leone Zabot ◽  
Marcus Vinícius Tres
Keyword(s):  
Meteorological Data ◽  
Accurate Determination ◽  
Principal Component ◽  
Empirical Models ◽  
Mean Bias Error ◽  
Bias Error ◽  
Southern Brazil ◽  
Agricultural Regions ◽  
Reference Crop

The FAO56 Penman-Monteith model is globally accepted for the accurate determination of reference evapotranspiration (ETo). However, a lack of appropriate data encouraged the improved model’s approach to estimate ETo. This study compared the performance of 10 empirical models of ETo estimation (Penman, Priestley & Taylor, Tanner & Pelton, Makkink, Jensen & Haise, Hargreaves & Samani, Camargo, Benevides & Lopes, Turc, and Linacre) contrasted with the FAO56 model in two regions in Southern Brazil. Data were collected from automatic stations of the Brazilian National Institute of Meteorology (INMET) from December 21, 2019, to February 28, 2021. The determination coefficient (R²), mean square error (nRMSE), mean bias error (MBE), Willmott index (d), and Pearson’s correlation coefficient (r), clustering, and Principal Component Analysis (PCA) were performed. For the different regions, the radiation-based model proposed by Penman was the best alternative for estimating ETo. The model showed the most appropriated values for R2 (0.9015) and r (0.9494). The clustering and PCA analyses indicated the interrelations of the meteorological data and the combination of the models according to the parameters used for the determination of ETo.

Sign in / Sign up

Export Citation Format

Share Document