scholarly journals Calibration of Ångström-Prescott Equation to Estimate Daily Solar Radiation on Rio Grande do Norte State, Brazil

2017 ◽  
Vol 32 (3) ◽  
pp. 409-416 ◽  
Author(s):  
Felipe J. de Medeiros ◽  
Claudio M. Santos e Silva ◽  
Bergson G. Bezerra
Keyword(s):  
Solar Radiation ◽  
Sunshine Duration ◽  
Rio Grande ◽  
Global Solar Radiation ◽  
Mean Bias Error ◽  
Bias Error ◽  
Space Resolution ◽  
Radiation Data ◽  
Abstract Knowledge

Abstract Knowledge of solar radiation is required for many applications. However, this atmospheric variable is not measured with an adequate space resolution. In this sense, to sites where solar radiation data are not directly measure, estimative using Ångström-Prescott equation can be used in order to provide solar radiation data, with input of sunshine duration. Thus, the objective of present study was to calibrate the Ångström-Prescott equation for different sites in Rio Grande do Norte state, Brazil. The performance of the calibrated Ångström-Prescott equation was evaluated by comparing of daily global solar radiation observed in situ. The MBE (Mean Bias Error) was lower than 1.50 MJ m-2 day-1, the Pearson's correlation coefficient about 0.90 and Willmott's index of agreement higher than 0.90, which are considered satisfactory.

scholarly journals Estimates of daily global solar radiation based on the daily temperature extremes and precipitation sums

2004 ◽  
Vol 52 (5) ◽  
pp. 13-28
Author(s):  
Miroslav Trnka
Keyword(s):  
Solar Radiation ◽  
Sunshine Duration ◽  
Regression Line ◽  
Global Solar Radiation ◽  
Daily Temperature ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Daily Maximum ◽  
Bias Error ◽  
Temperature Extremes

Two methods for estimating daily global solar radiation (RG) based on the daily temperature extremes and precipitation sum are compared in the study. All parameters necessary for application of both methods were derived either from literature or from climatic characteristics easily available at the given meteorological stations excluding need for measured RG data. The performance of both methods was assessed with a help of meteorological database including 4 stations in the Czech Republic (data were provided by the Czech Hydrometeorological Institute) and 6 in Austria (data provided by the Austrian Weather Service) containing in total 41 640 observational day. For each day in the database observed daily sum of RG, daily maximum and minimum temperatures and precipitation sum were available. Coefficient of determination, slope of regression line forced through origin, mean bias error (MBE) and root mean square error (RMSE) were used as performance indicators. The first method proposed by Winslow et al. (2001) – Eq. (1) is capable to explain 86% of daily RG variability, with systematic error represented by MBE equaling to 0.19 MJ.m–2.day-1 and random error indicated by RMSE reaching up to 3.09. The second method published by Thornton and Running (1999)-Eq. (2) was found to be in almost all parameters inferior to the Eq. (1) and thus the Eq. (1) is recommended to be used in the Central European region (up to 600 m above the sea level). This method might be recommended for stations where neither measured RG or sunshine duration hours exist. However, one should take into consideration that relative MBE and RMSE are in some months higher than 10% and 30% respectively, which may compromise results of subsequent calculations made with use of estimated solar radiation data and alter the order of the method suitability.

scholarly journals A Comparative Study of Some Regression Models to Estimate the Global Solar Radiation on a Horizontal Surface from Sunshine Duration and Meteorological Parameters for Ghardaïa Site, Algeria

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Kacem Gairaa ◽  
Yahia Bakelli
Keyword(s):  
Solar Radiation ◽  
Horizontal Plane ◽  
Regression Models ◽  
Meteorological Parameters ◽  
Sunshine Duration ◽  
Global Solar Radiation ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Quadratic Models

A comparison between some regression correlations for predicting the global solar radiation received on a horizontal plane has been processed. Seven models for estimating the global solar radiation from sunshine duration and two meteorological parameters (air temperature and relative humidity) are presented. The root mean square error (RMSE), mean bias error (MBE), correlation coefficient (CC), and percentage error () have been also computed to test the accuracy of the proposed models. Comparisons between the measured and the calculated values have been made. The results obtained show that the linear and quadratic models are the most suitable for estimating the global solar radiation from sunshine duration, and for the models based on meteorological parameters, Abdalla and Ojosu's models give the best performance with a CC of 0.898 and 0.892, respectively.

scholarly journals Comparative Study of Ground Measured, Satellite-Derived, and Estimated Global Solar Radiation Data in Nigeria

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Boluwaji M. Olomiyesan ◽  
Onyedi D. Oyedum
Keyword(s):  
Solar Radiation ◽  
Comparative Study ◽  
Global Radiation ◽  
Statistical Error ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Radiation Data ◽  
Proposed Model

In this study, the performance of three global solar radiation models and the accuracy of global solar radiation data derived from three sources were compared. Twenty-two years (1984–2005) of surface meteorological data consisting of monthly mean daily sunshine duration, minimum and maximum temperatures, and global solar radiation collected from the Nigerian Meteorological (NIMET) Agency, Oshodi, Lagos, and the National Aeronautics Space Agency (NASA) for three locations in North-Western region of Nigeria were used. A new model incorporating Garcia model into Angstrom-Prescott model was proposed for estimating global radiation in Nigeria. The performances of the models used were determined by using mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE), and coefficient of determination (R2). Based on the statistical error indices, the proposed model was found to have the best accuracy with the least RMSE values (0.376 for Sokoto, 0.463 for Kaduna, and 0.449 for Kano) and highest coefficient of determination, R2 values of 0.922, 0.938, and 0.961 for Sokoto, Kano, and Kaduna, respectively. Also, the comparative study result indicates that the estimated global radiation from the proposed model has a better error range and fits the ground measured data better than the satellite-derived data.

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 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.

scholarly journals Solar Blue Light Radiation Enhancement during Mid to Low Solar Elevation Periods under Cloud Affected Skies

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4105 ◽  
Author(s):  
Alfio V. Parisi ◽  
Damien P. Igoe ◽  
Abdurazaq Amar ◽  
Nathan J. Downs
Keyword(s):  
Solar Radiation ◽  
Visible Spectrum ◽  
Global Solar Radiation ◽  
Early Morning ◽  
High Energy ◽  
Mean Bias Error ◽  
Bias Error ◽  
The Sun ◽  
Solar Elevation ◽  
Ocular Health

Solar blue-violet wavelengths (380−455 nm) are at the high energy end of the visible spectrum; referred to as “high energy visible” (HEV). Both chronic and acute exposure to these wavelengths has been often highlighted as a cause for concern with respect to ocular health. The sun is the source of HEV which reaches the Earth’s surface either directly or after scattering by the atmosphere and clouds. This research has investigated the effect of clouds on HEV for low solar elevation (solar zenith angles between 60° and 80°), simulating time periods when the opportunity for ocular exposure in global populations with office jobs is high during the early morning and late afternoon. The enhancement of “bluing” of the sky due to the influence of clouds was found to increase significantly with the amount of cloud. A method is presented for calculating HEV irradiance at sub-tropical latitudes from the more commonly measured global solar radiation (300–3000 nm) for all cases when clouds do and do not obscure the sun. The method; when applied to global solar radiation data correlates well with measured HEV within the solar zenith angle range 60° and 80° (R2 = 0.82; mean bias error (MBE) = −1.62%, mean absolute bias error (MABE) = 10.3% and root mean square error (RMSE) = 14.6%). The technique can be used to develop repeatable HEV hazard evaluations for human ocular health applications

Evaluation of the regression parameters of the Angstrom-Page model for predicting global solar radiation

2013 ◽  
Vol 24 (2) ◽  
pp. 46-49 ◽  
Author(s):  
Solomon Agbo
Keyword(s):  
Solar Radiation ◽  
Statistical Tests ◽  
Global Solar Radiation ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Statistical Parameters ◽  
New Model ◽  
Page Model ◽  
Regression Parameters

A simple and empirical model for the estimation of average monthly global solar radiation for a Nigerian location is presented. Regression coefficients satisfying the Angstrom-page model have been obtained using clearness index (KT) and the relative sunshine data for the location. The test of validity of the model was done by evaluating the following statistical parameters: the mean bias error (MBE), root mean square error (RMSE), mean percentage error (MPE) and the correlation coefficient (CC). The results obtained from the statistical tests show that the new model is reliable for high precision estimation of global solar radiation. A comparison between the new model and other models is presented.

Evaluation of Global Solar Radiation using Sunshine Hour, Temperature and Relative Humidity at Low Land Region of Nepal

2020 ◽  
Vol 6 (1) ◽  
pp. 16-24
Author(s):  
U. Joshi ◽  
K.N. Poudyal ◽  
I.B. Karki ◽  
N.P. Chapagain
Keyword(s):  
Solar Radiation ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Energy Potential ◽  
To Come ◽  
Empirical Constants

The accurate knowledge of solar energy potential is essential for agricultural scientists, energy engineers, architects and hydrologists for relevant applications in concerned fields. It is cleanest and freely available renewable energy measured using CMP6 Pyranometer. However, it is quite challenging to acquire accurate solar radiation data in different locations of Nepal because of the high cost of instruments and maintenances. In these circumstances, it is essential to select an appropriate empirical model to predict global solar radiation for the use of future at low land, Nepalgunj (28.102°N, 81.668°E and alt. 165 masl) for the year 2011-2012. In this paper, six different empirical models have been used based on regression technique, provided the meteorological data. The empirical constants (a = 0.61, b = 0.05, c = -0.0012 and d = -0.017) are obtained to predict Global solar radiation. The values of statistical tools such as mean percentage error, mean bias error, root mean square error, and coefficient of determination obtained for Abdalla model are 1.99%, 0.003 MJ/m2/day, 2.04 MJ/m2/day and 0.74 respectively. Using the error analysis, it is concluded that the Abdalla model is better than others. So the empirical constants of this model are utilized to predict the global solar radiation to the similar geographical sites of Nepal for the years to come and it can be used to estimate the missing data of solar radiation for the respective sites.

scholarly journals Choice of ideal sunshine hour based model to predict global solar radiation in India

MAUSAM ◽  
2021 ◽  
Vol 71 (3) ◽  
pp. 451-466
Author(s):  
SAMANTA SUMAN ◽  
BANERJEE SAON ◽  
PATRA PULAK KUMAR ◽  
MAITI SUDHANSU SEKHAR ◽  
CHATTOPADHYAY NABANSU
Keyword(s):  
Energy Source ◽  
Solar Radiation ◽  
Energy Demand ◽  
Global Solar Radiation ◽  
Exponential Model ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Energy Conversion Systems ◽  
Sunshine Hour

Solar radiation is the key energy source for most of the energy conversion systems, whether it is biological or mechanical. It is also the most fundamental energy source for future energy demand. Like most of the developing countries, India also lacks sufficient instrument facilities to measure global solar radiation (GSR) at recommended spatial interval and alternative approaches must be used to generate GSR data. In the present study, six well known empirical models were tested to estimate the GSR over twelve major cities of India using long-term global solar radiation and bright sunshine hour data. The empirical coefficients have been calculated for all the models and each location using regression analysis method. Daily GSR are then calculated using those regression constants along with statistical analysis. Results reveal that all the models shows close estimation with low mean bias error (MBE), root mean square error (RMSE) and mean percentage error (MPE) values. Among all models, linear exponential and linear logarithmic models are highly recommended for prediction of GSR throughout the country, except Shillong, where Bakircilinear exponential model is recommended. Significance tests i.e., t-test also confirms that this two model produce most significant results than others.

scholarly journals Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data

2015 ◽  
Vol 15 (23) ◽  
pp. 35201-35236
Author(s):  
W. Tang ◽  
J. Qin ◽  
K. Yang ◽  
S. Liu ◽  
N. Lu ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Temporal Resolution ◽  
Surface Radiation ◽  
Mean Bias Error ◽  
Haihe River Basin ◽  
Bias Error ◽  
High Temporal Resolution ◽  
Surface Solar Radiation ◽  
Radiation Data ◽  
Cloud Parameters

Abstract. Cloud parameters (cloud mask, effective particle radius and liquid/ice water path) are the important inputs in determining surface solar radiation (SSR). These parameters can be derived from MODIS with high accuracy but their temporal resolution is too low to obtain high temporal resolution SSR retrievals. In order to obtain hourly cloud parameters, the Artificial Neural Network (ANN) is applied in this study to directly construct a functional relationship between MODIS cloud products and Multi-functional Transport Satellite (MTSAT) geostationary satellite signals. Meanwhile, an efficient parameterization model for SSR retrieval is introduced and, when driven with MODIS atmospheric and land products, its root mean square error (RMSE) is about 100 W m-2 for 44 Baseline Surface Radiation Network (BSRN) stations. Once the estimated cloud parameters and other information (such as aerosol, precipitable water, ozone and so on) are input to the model, we can derive SSR at high spatio-temporal resolution. The retrieved SSR is first evaluated against hourly radiation data at three experimental stations in the Haihe River Basin of China. The mean bias error (MBE) and RMSE in hourly SSR estimate are 12.0 W m-2 (or 3.5 %) and 98.5 W m-2 (or 28.9 %), respectively. The retrieved SSR is also evaluated against daily radiation data at 90 China Meteorological Administration (CMA) stations. The MBEs are 9.8 W m-2 (5.4 %); the RMSEs in daily and monthly-mean SSR estimates are 34.2 W m-2 (19.1 %) and 22.1 W m-2 (12.3 %), respectively. The accuracy is comparable or even higher than other two radiation products (GLASS and ISCCP-FD), and the present method is more computationally efficient and can produce hourly SSR data at a spatial resolution of 5 km.

Sign in / Sign up

Export Citation Format

Share Document