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 Surface Diffuse Solar Radiation Determined by Reanalysis and Satellite over East Asia: Evaluation and Comparison

2020 ◽  
Vol 12 (9) ◽  
pp. 1387
Author(s):  
Hou Jiang ◽  
Yaping Yang ◽  
Hongzhi Wang ◽  
Yongqing Bai ◽  
Yan Bai
Keyword(s):  
Solar Radiation ◽  
East Asia ◽  
Regional Scale ◽  
Energy Utilization ◽  
Mean Bias Error ◽  
Bias Error ◽  
China Meteorological Administration ◽  
Diffuse Solar Radiation ◽  
Weather Forecasts ◽  
Solar Energy Utilization

Recently, surface diffuse solar radiation (Rdif) has been attracting a growing interest in view of its function in improving plant productivity, thus promoting global carbon uptake, and its impacts on solar energy utilization. To date, very few radiation products provide estimates of Rdif, and systematic validation and evaluation are even more scare. In this study, Rdif estimates from Reanalysis Fifth Generation (ERA5) of European Center for Medium-Range Weather Forecasts and satellite-based retrieval (called JiEA) are evaluated over East Asia using ground measurements at 39 stations from World Radiation Data Center (WRDC) and China Meteorological Administration (CMA). The results show that JiEA agrees well with measurements, while ERA5 underestimates Rdif significantly. Both datasets perform better at monthly mean scale than at daily mean and hourly scale. The mean bias error and root-mean-square error of daily mean estimates are −1.21 W/m2 and 20.06 W/m2 for JiEA and −17.18 W/m2 and 32.42 W/m2 for ERA5, respectively. Regardless of over- or underestimation, correlations of estimated time series of ERA5 and JiEA show high similarity. JiEA reveals a slight decreasing trend at regional scale, but ERA5 shows no significant trend, and neither of them reproduces temporal variability of ground measurements. Data accuracy of ERA5 is more robust than JiEA in time but less in space. Latitudinal dependency is noted for ERA5 while not for JiEA. In addition, spatial distributions of Rdif from ERA5 and JiEA show pronounced discrepancy. Neglect of adjacency effects caused by horizontal photon transport is the main cause for Rdif underestimation of ERA5. Spatial analysis calls for improvements to the representation of clouds, aerosols and water vapor for reproducing fine spatial distribution and seasonal variations of Rdif.

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 Performance Evaluation of Models Established for the Estimation of Diffused Solar Radiation: Case Study Lahore, Pakistan

2019 ◽  
Vol 10 (1) ◽  
pp. 113-119
Author(s):  
Saif Ur Rehman ◽  
Muhammad Shoaib ◽  
Imran Siddiqui ◽  
S. Zeeshan Abbas
Keyword(s):  
Solar Radiation ◽  
Goodness Of Fit ◽  
Statistical Error ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Clearness Index ◽  
Diffuse Solar Radiation

A suitable design of solar power project requires accurate measurements of solar radiation for the site ofinvestigation. Such measurements play a pivotal role in the installation of PV systems. While conducting such studies,in general, global solar radiation (GSR) is recorded, whereas diffuse component of solar radiation on a horizontalsurface is seldom recorded. The objective of the present study is to assess diffuse solar radiation (DSR) on horizontalsurfaces by using polynomial models for Lahore, Pakistan (27.89 N, 78.08 E) and by correlating clearness index withdiffuse fraction. The established models are compared with some of the existing models from the literature.Performance of models is evaluated by employing five goodness-of-fit (GoF) tests that are, mean bias error (MBE),root mean square (RMSE), Coefficient of Determination (R2), Mean Absolute Percentage Error (MAPE) and Akaike’sInformation Criterion (AIC). The comparison of the results of goodness-of-fit tests with those of existing modelsindicate that the models established in the present study are performed better as compared to the existing models. Thevalues of statistical error analysis further suggested that a cubic model with a good accuracy of 97.5% and AIC of -22.8is relatively more suitable for this climatic region for estimating diffuse solar radiation. The study shows that the modeldeveloped is in good agreement with Elhadidy and Nabi model with an accuracy of 96.1% and AIC of 4.4 andsatisfactory results are obtained for Lahore. The findings can help to give a generous understanding of solar radiation inorder to optimize the solar energy conversion systems. The results of this study provide a better understanding of theassociations between global solar radiation, clearness index and diffused fraction for the region under study.

scholarly journals Performance Evaluation of Models Established for the Estimation of Diffused Solar Radiation: Case Study Lahore, Pakistan

2019 ◽  
Vol 10 (1) ◽  
pp. 113-119
Author(s):  
Saif Ur Rehman ◽  
Muhammad Shoaib ◽  
Imran Siddiqui ◽  
S. Zeeshan Abbas
Keyword(s):  
Solar Radiation ◽  
Goodness Of Fit ◽  
Statistical Error ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Clearness Index ◽  
Diffuse Solar Radiation

A suitable design of solar power project requires accurate measurements of solar radiation for the site ofinvestigation. Such measurements play a pivotal role in the installation of PV systems. While conducting such studies,in general, global solar radiation (GSR) is recorded, whereas diffuse component of solar radiation on a horizontalsurface is seldom recorded. The objective of the present study is to assess diffuse solar radiation (DSR) on horizontalsurfaces by using polynomial models for Lahore, Pakistan (27.89 N, 78.08 E) and by correlating clearness index withdiffuse fraction. The established models are compared with some of the existing models from the literature.Performance of models is evaluated by employing five goodness-of-fit (GoF) tests that are, mean bias error (MBE),root mean square (RMSE), Coefficient of Determination (R2), Mean Absolute Percentage Error (MAPE) and Akaike’sInformation Criterion (AIC). The comparison of the results of goodness-of-fit tests with those of existing modelsindicate that the models established in the present study are performed better as compared to the existing models. Thevalues of statistical error analysis further suggested that a cubic model with a good accuracy of 97.5% and AIC of -22.8is relatively more suitable for this climatic region for estimating diffuse solar radiation. The study shows that the modeldeveloped is in good agreement with Elhadidy and Nabi model with an accuracy of 96.1% and AIC of 4.4 andsatisfactory results are obtained for Lahore. The findings can help to give a generous understanding of solar radiation inorder to optimize the solar energy conversion systems. The results of this study provide a better understanding of theassociations between global solar radiation, clearness index and diffused fraction for the region under study.

scholarly journals A 16-year dataset (2000–2015) of high-resolution (3 h, 10 km) global surface solar radiation

2019 ◽  
Vol 11 (4) ◽  
pp. 1905-1915 ◽  
Author(s):  
Wenjun Tang ◽  
Kun Yang ◽  
Jun Qin ◽  
Xin Li ◽  
Xiaolei Niu
Keyword(s):  
High Resolution ◽  
Solar Radiation ◽  
Radiant Energy ◽  
Reanalysis Data ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Mean Bias Error ◽  
Bias Error ◽  
Surface Solar Radiation ◽  
Global Surface

Abstract. The recent release of the International Satellite Cloud Climatology Project (ISCCP) HXG cloud products and new ERA5 reanalysis data enabled us to produce a global surface solar radiation (SSR) dataset: a 16-year (2000–2015) high-resolution (3 h, 10 km) global SSR dataset using an improved physical parameterization scheme. The main inputs were cloud optical depth from ISCCP-HXG cloud products; the water vapor, surface pressure and ozone from ERA5 reanalysis data; and albedo and aerosol from Moderate Resolution Imaging Spectroradiometer (MODIS) products. The estimated SSR data were evaluated against surface observations measured at 42 stations of the Baseline Surface Radiation Network (BSRN) and 90 radiation stations of the China Meteorological Administration (CMA). Validation against the BSRN data indicated that the mean bias error (MBE), root mean square error (RMSE) and correlation coefficient (R) for the instantaneous SSR estimates at 10 km scale were −11.5 W m−2, 113.5 W m−2 and 0.92, respectively. When the estimated instantaneous SSR data were upscaled to 90 km, its error was clearly reduced, with RMSE decreasing to 93.4 W m−2 and R increasing to 0.95. For daily SSR estimates at 90 km scale, the MBE, RMSE and R at the BSRN were −5.8 W m−2, 33.1 W m−2 and 0.95, respectively. These error metrics at the CMA radiation stations were 2.1 W m−2, 26.9 W m−2 and 0.95, respectively. Comparisons with other global satellite radiation products indicated that our SSR estimates were generally better than those of the ISCCP flux dataset (ISCCP-FD), the global energy and water cycle experiment surface radiation budget (GEWEX-SRB), and the Earth's Radiant Energy System (CERES). Our SSR dataset will contribute to the land-surface process simulations and the photovoltaic applications in the future. The dataset is available at  https://doi.org/10.11888/Meteoro.tpdc.270112 (Tang, 2019).

scholarly journals Estimation of monthly global solar radiation in Buenos Aires: preliminary analysis

2014 ◽  
Vol 5 (1) ◽  
pp. 669-680
Author(s):  
Susan G. Lakkis ◽  
Mario Lavorato ◽  
Pablo O. Canziani
Keyword(s):  
Solar Radiation ◽  
Buenos Aires ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Estimation Accuracy ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Third Order ◽  
Sunshine Hours

Six existing models and one proposed approach for estimating global solar radiation were tested in Buenos Aires using commonly measured meteorological data as temperature and sunshine hours covering the years 2010-2013. Statistical predictors as mean bias error, root mean square, mean percentage error, slope and regression coefficients were used as validation criteria. The variability explained (R2), slope and MPE indicated that the higher precision could be excepted when sunshine hours are used as predictor. The new proposed approach explained almost 99% of the RG variability with deviation of less than ± 0.1 MJm-2day-1 and with the MPE smallest value below 1 %. The well known Ångström-Prescott methods, first and third order, was also found to perform for the measured data with high accuracy (R2=0.97-0.99) but with slightly higher MBE values (0.17-0.18 MJm-2day-1). The results pointed out that the third order Ångström type correlation did not improve the estimation accuracy of solar radiation given the highest range of deviation and mean percentage error obtained.  Where the sunshine hours were not available, the formulae including temperature data might be considered as an alternative although the methods displayed larger deviation and tended to overestimate the solar radiation behavior.

Wind power generation forecast by coupling numerical weather prediction model and gradient boosting machines in Yahyalı wind power plant

2020 ◽  
pp. 0309524X2097211
Author(s):  
Cem Özen ◽  
Umur Dinç ◽  
Ali Deniz ◽  
Haldun Karan
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Hybrid Model ◽  
Spatial Resolution ◽  
Numerical Weather Prediction ◽  
Wind Farm ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Wind Power Generation ◽  
Gradient Boosting

Forecasting of the wind speed and power generation for a wind farm has always been quite challenging and has importance in terms of balancing the electricity grid and preventing energy imbalance penalties. This study focuses on creating a hybrid model that uses both numerical weather prediction model and gradient boosting machines (GBM) for wind power generation forecast. Weather Research and Forecasting (WRF) model with a low spatial resolution is used to increase temporal resolutions of the computed new or existing variables whereas GBM is used for downscaling purposes. The results of the hybrid model have been compared with the outputs of a stand-alone WRF which is well configured in terms of physical schemes and has a high spatial resolution for Yahyalı wind farm over a complex terrain located in Turkey. Consequently, the superiority of the hybrid model in terms of both performance indicators and computational expense in detail is shown.

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.

Evaluation of Different Radiation and Albedo Models for the Prediction of Solar Radiation Incident on Tilted Surfaces, for Four European Locations

1996 ◽  
Vol 118 (3) ◽  
pp. 183-189 ◽  
Author(s):  
B. E. Psiloglou ◽  
C. A. Balaras ◽  
M. Santamouris ◽  
D. N. Asimakopoulos
Keyword(s):  
Solar Radiation ◽  
Diffuse Radiation ◽  
Vertical Surface ◽  
Mean Bias Error ◽  
Bias Error ◽  
Total Solar Radiation ◽  
Total Radiation ◽  
Radiation Incident ◽  
Tilted Surface ◽  
Significant Difference

The diffuse radiation incident on an inclined surface is composed of both the sky diffuse radiation and the ground-reflected radiation. Depending on the model used to calculate the sky diffuse radiation and the estimated albedo value, it is possible to introduce a significant error in the prediction of the total radiation incident on a tilted surface. Twelve sky diffuse submodels associated with four different albedo submodels are used to estimate the total radiation on the tilted surface from data on the horizontal plane. The predicted total solar radiation values are compared with measured data on a south facing vertical surface, from four representative south and north European locations. Root mean square error, mean bias error, and a t-test are used to determine the intrinsic performance of each combination of diffuse tilt and albedo submodel. Accordingly, the various model combinations do not exhibit a statistically significant difference between measured and calculated values.

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