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.

Developing and Evaluation of Various Correlations for Diffuse Solar Radiation for Urla (Izmir, Turkey)

2012 ◽  
Vol 135 (2) ◽  
Author(s):  
Orhan Ekren
Keyword(s):  
Solar Radiation ◽  
Solar Energy ◽  
Tracking System ◽  
Diffuse Radiation ◽  
Energy System ◽  
Solar Irradiation ◽  
Total Solar Radiation ◽  
Total Radiation ◽  
Diffuse Solar Radiation ◽  
Tilted Surface

Characteristics of site-specific solar irradiation is required to optimize a solar energy system. If no tracking system is used, the amount of electricity or heat produced by solar energy depends on the total solar radiation on a tilted surface. Although pyranometer measures direct plus diffuse solar radiation on a horizontal surface, there are many locations where diffuse radiation is not measured. Also, diffuse radiation is necessary to determine the total radiation on a tilted surface. Therefore, in this study, new correlations for diffuse solar radiation is proposed as a function of atmospheric parameters for Urla (Izmir, Turkey). After applying the statistical procedure on the measured data, seven new correlations are proposed for the ratio of hourly average diffuse and total radiation. Also, the ratio of monthly average daily diffuse and total radiation for this region is proposed.

scholarly journals Long-term variations in solar radiation, diffuse radiation, and diffuse radiation fraction caused by aerosols in China during 1961–2016

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250376
Author(s):  
Hongfei Xie ◽  
Junfang Zhao ◽  
Kaili Wang ◽  
Huiwen Peng
Keyword(s):  
Solar Radiation ◽  
Atmospheric Aerosols ◽  
Terrestrial Ecosystem ◽  
Diffuse Radiation ◽  
Total Solar Radiation ◽  
Total Radiation ◽  
Negative Effects ◽  
Positive Effects ◽  
Sunshine Hours ◽  
The Impact

The effects of atmospheric aerosols on the terrestrial climate system are more regional than those of greenhouse gases, which are more global. Thus, it is necessary to examine the typical regional effects of how aerosols affect solar radiation in order to develop a more comprehensive understanding. In this study, we used global AErosol RObotic NETwork (AERONET) data and robust radiation observational evidence to investigate the impact of aerosols on total radiation, diffuse radiation, and the diffuse radiation fraction in China from 1961 to 2016. Our results showed that there were different temporal changes in the aerosol optical depth (AOD), total solar radiation, diffuse radiation and diffuse radiation fraction over the past 56 years. Specifically, the 550 nm AOD from 2005 to 2016 decreased significantly, with annual average AOD of 0.51. Meanwhile, the average total solar radiation reduced by 2.48%, while there was a slight increase in average diffuse radiation at a rate of 3.10 MJ·m-2·yr-1. Moreover, the spatial heterogeneities of AOD, total radiation, diffuse radiation, and the diffuse radiation fraction in China were significant. Aerosol particle emissions in the developed eastern and southern regions of China were more severe than those in the western regions, resulting in higher total radiation and diffuse radiation in the western plateau than in the eastern plain. In addition, aerosols were found to have negative effects on total radiation and sunshine hours, and positive effects on diffuse radiation and diffuse radiation fraction. Further, the diffuse radiation fraction was negatively correlated with sunshine hours. However, there was a positive correlation between AOD and sunshine hours. These results could be used to assess the impacts of climate change on terrestrial ecosystem productivity and carbon budgets.

scholarly journals Estimation of Solar Radiation on Tilted Surface by Using Regression Analysis at Different Locations in India

2020 ◽  
Author(s):  
Abdul Qadeer ◽  
Mohammad Emran Khan ◽  
Shah Alam
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Climatic Conditions ◽  
Data Availability ◽  
Mean Bias Error ◽  
Bias Error ◽  
New Delhi ◽  
Mean Values ◽  
Tilted Surface ◽  
Regression Techniques

This study is to find the regression model for estimation of monthly mean hourly global solar radiation on tilted surface at different locations of India. This study is quite precious due to lack of solar radiation data availability on the tilted surface. Firstly, we have selected some locations having different climatic conditions such as New Delhi, Mumbai, Kolkata, Lucknow and Jaipur to find the solar radiation on tilted surface using Liu and Jordan model, HDKR model and Perez model. The mean values of these models are plotted along with the daytime. Based on regression techniques, four empirical models are developed which are tested to compute the solar radiation on tilted surface for three new stations Ahmadabad, Bangalore and Chennai. The estimated solar radiation by these four developed models are compared with the estimated values using existing models Lie & Jordan, HDKR and Perez based on mean bias error (MBE) and root mean square error (RMSE). It has been found that developed Model-3 has minimum error and the values estimated this model is comparable to existing models. The maximum values of RMSE in Model-3 in tested stations are 2.01% with Liu and Jordan, 2.63% with HDKR and 2.10% with Perez. Similarly, maximum values of MBE are −1.79% with Liu and Jordan, −2.27% with HDKR and −1.89% with Perez. Now the Model-3 finally selected to determine the solar radiation on Bhopal, Bhubneshwar, Dehradun, Guwahati and Trivendrum (Thiruvananthapuram).

scholarly journals Hourly 5-km surface total and diffuse solar radiation in China, 2007–2018

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Hou Jiang ◽  
Ning Lu ◽  
Jun Qin ◽  
Ling Yao
Keyword(s):  
Solar Radiation ◽  
Correlation Coefficient ◽  
Numerical Models ◽  
Regional Climate ◽  
Terrestrial Ecosystem ◽  
Mean Bias Error ◽  
Bias Error ◽  
Total Solar Radiation ◽  
Surface Solar Radiation ◽  
Diffuse Solar Radiation

Abstract Surface solar radiation is an indispensable parameter for numerical models, and the diffuse component contributes to the carbon uptake in ecosystems. We generated a 12-year (2007–2018) hourly dataset from Multi-functional Transport Satellite (MTSAT) satellite observations, including surface total solar radiation (Rs) and diffuse radiation (Rdif), with 5-km spatial resolution through deep learning techniques. The used deep network tacks the integration of spatial pattern and the simulation of complex radiation transfer by combining convolutional neural network and multi-layer perceptron. Validation against ground measurements shows the correlation coefficient, mean bias error and root mean square error are 0.94, 2.48 W/m2 and 89.75 W/m2 for hourly Rs and 0.85, 8.63 W/m2 and 66.14 W/m2 for hourly Rdif, respectively. The correlation coefficient of Rs and Rdif increases to 0.94 (0.96) and 0.89 (0.92) at daily (monthly) scales, respectively. The spatially continuous hourly maps accurately reflect regional differences and restore the diurnal cycles of solar radiation at fine resolution. This dataset can be valuable for studies on regional climate changes, terrestrial ecosystem simulations and photovoltaic applications.

scholarly journals Estimation of the Daily Global Solar Radiation using RadEst 3.00 Software at Nepalgunj, Nepal

2017 ◽  
Vol 12 (1) ◽  
pp. 199-209
Author(s):  
Bed Raj KC ◽  
Shekhar Gurung
Keyword(s):  
Solar Radiation ◽  
Geographical Location ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Model Parameters ◽  
Total Solar Radiation ◽  
Radiation Distribution ◽  
Atmospheric Transmissivity

The RadEst 3.00 version software estimates daily total solar radiation at low land area using meteorological parameters such as precipitation, temperatures and solar radiation of Nepalgunj (Lat.28.05°N, Lon.81.62°E, and Alt.150m). Radiation is calculated as the product of the atmospheric transmissivity of radiation and radiation outside earth atmosphere. The model parameters are fitted in two years data. An accurate knowledge of solar radiation distribution in each particular geographical location is crucial for the promotion of solar active and passive energy technology. The values estimated by the models are compared with measured solar radiation data. The performance of the model was evaluated using root mean square error (RMSE), mean bias error (MBE), Coefficient of Residual Mass (CRM) and coefficient of determination (R2). The RadEst 3.0 software which showed the better results using BC, CD, DB and DCBB, among them the DCBB model is the best model for this site. The values of RMSE, MBE, CRM and R2are 5.20, 3.98, 0.00 and 0.47 respectively. The finding coefficients of different models can be utilized for the estimation of solar radiation at the similar meteorological sites of Nepal.Journal of the Institute of Engineering, 2016, 12(1): 199-209

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.

scholarly journals Assessment of Artificial Neural Networks for Hourly Solar Radiation Prediction

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Tamer Khatib ◽  
Azah Mohamed ◽  
K. Sopian ◽  
M. Mahmoud
Keyword(s):  
Neural Networks ◽  
Solar Radiation ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Climate Conditions ◽  
The Neural Networks ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Radiation Conditions

This paper presents an assessment for the artificial neural network (ANN) based approach for hourly solar radiation prediction. The Four ANNs topologies were used including a generalized (GRNN), a feed-forward backpropagation (FFNN), a cascade-forward backpropagation (CFNN), and an Elman backpropagation (ELMNN). The three statistical values used to evaluate the efficacy of the neural networks were mean absolute percentage error (MAPE), mean bias error (MBE) and root mean square error (RMSE). Prediction results show that the GRNN exceeds the other proposed methods. The average values of the MAPE, MBE and RMSE using GRNN were 4.9%, 0.29% and 5.75%, respectively. FFNN and CFNN efficacies were acceptable in general, but their predictive value was degraded in poor solar radiation conditions. The average values of the MAPE, MBE and RMSE using the FFNN were 23%, −.09% and 21.9%, respectively, while the average values of the MAPE, MBE and RMSE using CFNN were 22.5%, −19.15% and 21.9%, respectively. ELMNN fared the worst among the proposed methods in predicting hourly solar radiation with average MABE, MBE and RMSE values of 34.5%, −11.1% and 34.35%. The use of the GRNN to predict solar radiation in all climate conditions yielded results that were highly accurate and efficient.

scholarly journals Empirical Model for Estimating Global Solar Radiation on Horizontal Surfaces for Selected Cities in the Six Geopolitical Zones in Nigeria

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
M. S. Okundamiya ◽  
A. N. Nzeako
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Mean Bias Error ◽  
Bias Error ◽  
Data Set ◽  
Ambient Temperatures ◽  
Performance Accuracy ◽  
Daily Data ◽  
Proposed Model ◽  
Regression Theory

This study proposes a temperature-based model of monthly mean daily global solar radiation on horizontal surfaces for selected cities, representing the six geopolitical zones in Nigeria. The modelling was based on linear regression theory and was computed using monthly mean daily data set for minimum and maximum ambient temperatures. The results of three statistical indicators: Mean Bias Error (MBE), Root Mean Square Error (RMSE), andt-statistic (TS), performed on the model along with practical comparison of the estimated and observed data, validate the excellent performance accuracy of the proposed model.

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