Measurement and assessment of ultraviolet radiation in Riyadh,Saudi Arabia

Measurements of ultraviolet solar radiation (UV radiation) of wavelengths 0.295–0.385?μm were recorded from a huge data set collected every 10 minutes for a long period of time (over 60 months) in Riyadh, Saudi Arabia (Lat. 24°6′?N Long. 46°7′?E). The daily and monthly average percent ratio of UV to global irradiance was discussed. Findings indicate that maximum UV radiation occurs in July (16.6?Wm^?2) and the minimum values occur in December (8.3?Wm^?2). The measured UV intensity in Riyadh was compared with those measured in different regional cities; Bahrain, Kuwait, Dhahran, Makkah (Al-Mukkaramah) and Cairo. The results of comparison were discussed and attributed to different causes. It shows that the data of Riyadh are in good agreement with those of Dhahran and Cairo.     

Global, direct and diffuse solar irradiance in Riyadh, Saudi Arabia

The regression coefficient of the well-known Angstrom correlation are determined for Riyadh city (the capital of Saudi Arabia) longitude 46° 14′E 24°55′N The relationship of the daily and monthly variation of the fraction of the diffuse solar irradiation to extrateresstrial and the clearness index are obtained. The variation of the values of the average daily global solar radiation against the month of the year is reported. The value of K_n defined as the ratio of direct normal Insolation, H_bn measured in Riyadh to direct normal solar extraterrestrial radiation with the K_T and K_d were obtained. The daily diffused ratio and the daily clearness index are shown as a function of the month of the year.     

Estimation of global solar radiation on horizontal surfaces in Jeddah, Saudi Arabia

The measured data of global solar radiation on a horizontal surface, as well as the number of sunshine hours, mean daily ambient temperature, maximum and minimum ambient temperatures, relative humidity and amount of cloud cover, for Jeddah (latitude 21° 42′37″N, longitude 39° 11′12″E), Saudi Arabia for the period 1996–2006 are analyzed. The data are divided into two sets. The sub-data set 1 (1996–2004) are employed to develop empirical correlations between the monthly average of daily global solar radiation fraction (H/H₀) and various meteorological parameters. The nonlinear Angström type model developed by Sen and the trigonometric function model proposed by Bulut and Büyükalaca are also evaluated. New empirical constants for these two models have been obtained for Jeddah. The sub-data set 2 (2005, 2006) are then used to evaluate the derived correlations. Comparisons between measured and calculated values of H have been performed. It is indicated that, the Sen and Bulut and Büyükalaca models satisfactorily describe the horizontal global solar radiation for Jeddah. All the proposed correlations are found to be able to predict the annual average of daily global solar radiation with excellent accuracy. Therefore, the long term performance of solar energy devices can be estimated.     

New methodologies to estimate the hourly global solar radiation; Comparisons with existing models

This paper describes two new friendly and reliable approaches to estimate hourly global solar radiation on a horizontal surface even with a pocket calculator.Such fast and reliable predictions for the hourly solar radiation are necessary for the real-time management of both the solar energy sources, like a PV generator output in the one hand and the power loads, on the other.The predicted global solar hourly radiation values are compared with estimates from two existing packages and the recorded solar radiation for the two biggest cities of Greece.The two methodologies presented in this paper can be applied to any other site.     

A simple and efficient algorithm to estimate daily global solar radiation from geostationary satellite data

Surface global solar radiation (GSR) is the primary renewable energy in nature. Geostationary satellite data are used to map GSR in many inversion algorithms in which ground GSR measurements merely serve to validate the satellite retrievals. In this study, a simple algorithm with artificial neural network (ANN) modeling is proposed to explore the non-linear physical relationship between ground daily GSR measurements and Multi-functional Transport Satellite (MTSAT) all-channel observations in an effort to fully exploit information contained in both data sets. Singular value decomposition is implemented to extract the principal signals from satellite data and a novel method is applied to enhance ANN performance at high altitude. A three-layer feed-forward ANN model is trained with one year of daily GSR measurements at ten ground sites. This trained ANN is then used to map continuous daily GSR for two years, and its performance is validated at all 83 ground sites in China. The evaluation result demonstrates that this algorithm can quickly and efficiently build the ANN model that estimates daily GSR from geostationary satellite data with good accuracy in both space and time.     

Correlations for estimation of daily global solar radiation with hours of bright sunshine in Turkey

In this study, seven different empirical equations are employed to estimate the monthly average daily global solar radiation on a horizontal surface for provinces in the different regions of Turkey, using only the relative duration of sunshine. Daily global solar radiation and sunshine measurement data collected for the provinces of Turkey are obtained from the Turkish State Meteorological Service. The regression constants of the new models developed in this study are found for the provinces of Turkey, as well as that of some models given in the literature. In order to indicate the performance of the models, the statistical test methods of the mean bias error (MBE), mean absolute bias error (MABE), mean relative error (MRE), root mean square error (RMSE) and correlation coefficient (r) are used.     

Investigating different diffuse solar radiation models to analyse solar radiation on inclined surfaces in Oman

In this paper, a detailed analysis of the solar radiation on horizontal and tilted surfaces for six locations in Oman is presented. The locations are (from North to South): Majis/Sohar, Sur, Fahud, Masira, Marmul, and Salalah. These locations spread over Oman and cover different types of landscape. The method is validated through the use of measured data. The effect of tilt angle and orientation on the incident solar radiation is presented along with optimum surface tilt angles and directions for maximum solar radiation collection in these six locations. The solar radiation models used in this paper show good agreement with measured data. The results presented in this paper are extremely useful for quick estimation of solar radiation for calculations of buildings’ cooling load and solar collector system performance. This can be easily extended for other locations with similar landscapes and geographical conditions.     

Computing global and diffuse solar hourly irradiation on clear sky. Review and testing of 54 models

Fifty-four broad band models for computation of global and diffuse irradiance on horizontal surface are shortly presented and tested. The input data for these models consist of surface meteorological data, atmospheric column integrated data and data derived from satellite measurements. The testing procedure is performed for two meteorological stations in Romania (South-Eastern Europe). The testing procedure consists of forty-two stages intended to provide information about the sensitivity of the models to various sets of input data. There is no model to be ranked “the best” for all sets of input data. Very simple models as well as more complex models may belong to the category of “good models”. The best models for solar global radiation computation are, on equal-footing, ESRA3, Ineichen, METSTAT and REST2 (version 81). The second best models are, on equal-footing, Bird, CEM and Paulescu & Schlett. The best models for solar diffuse radiation computation are, on equal-footing, ASHRAE2005 and King. The second best model is MAC model. The best models for computation of both global and diffuse radiation are, on equal-footing, ASHRAE 1972, Biga, Ineichen and REST2 (version 81). The second best is Paulescu & Schlett model.     

Evaluation and establishment of diffuse solar radiation models for Bursa,Turkey

The main objective of the present study is to evaluate the applicability of the diffuse solar radiation models from previous studies and establish new models for Bursa. Therefore, 35 regression models from previous studies in the literature are used and categorized as follows: (1) cloudness index is the function of clearness index; (2) cloudness index is the function of relative sunshine duration; (3) the diffuse coefficient is the function of the clearness index; and (4) the diffuse coefficient is the function of the relative sunshine duration. Also, new four diffuse solar radiation models were developed using 1968–2015 long-term global solar radiation and sunshine duration data. The new models are then compared with 35 empirical equations available in previous studies in terms of different statistical tests. Consequently, Models 6 and 8 are found as the most accurate and high-performance empirical models for estimation of the monthly mean diffuse solar radiation on a horizontal surface for Bursa, Turkey.     

Estimation of monthly solar radiation distribution for solar energy system analysis

The concept of probability density frequency, which is successfully used for analyses of wind speed and outdoor temperature distributions, is now modified and proposed for estimating solar radiation distributions for design and analysis of solar energy systems. In this study, global solar radiation distribution is comprehensively analyzed for photovoltaic (PV) panel and thermal collector systems. In this regard, a case study is conducted with actual global solar irradiation data of the last 15 years recorded by the Turkish State Meteorological Service. It is found that intensity of global solar irradiance greatly affects energy and exergy efficiencies and hence the performance of collectors.     

Monthly ratios of PAR to global solar radiation measured at northern Tibetan Plateau, China

Using narrowband and broadband solar radiation measurements collected at Wudaoliang (WDL) site in northern Tibetan Plateau (NTP) from September 1993 to December 1998, the relationship between monthly photosynthetically active radiation (Q_P) and global solar radiation (R_S) values is analyzed. Temporal variability of the ratio (Q_P/R_S) and its further dependence on several meteorological variables are presented. The narrowband ratio exhibited diurnal and seasonal variability with high values during morning and afternoon hours and low values around noon in winter time, whereas during summer period the ratio was decreased from morning to afternoon. The ratio (Q_P/R_S) was correlated positively with several atmospheric parameters such as water vapor pressure and low-level cloud amount; in contrast, the ratio was negatively correlated with clearness index, relative sunshine duration and atmospheric turbidity. It was also found that both the relative sunshine duration and water vapor pressure are the most influential parameters on the estimations of the spectral PAR ratio. Finally, an empirically derived model is proposed for estimating monthly average PAR values over the northern Tibetan Plateau. Verification results further ensured that the proposed model predicts monthly global PAR values accurately.     

Quality control of solar radiation data within the RMIB solar measurements network

Assessment of the solar resource is based upon measured data, where available. However, with any measurement there exist errors. Consequently, solar radiation data do not exhibit necessarily the same reliability and it often happens that users face time series of measurements containing questionable values though preliminary technical control has been done before the data release. To overcome such a situation, a major effort has been undertaken at the Royal Meteorological Institute of Belgium (RMIB) to develop procedures and software for performing post-measurement quality control of solar data from the radiometric stations of our in situ solar monitoring network. Moreover, because solar energy applications usually need continuous time series of solar radiation data, additional procedures have also been established to fill missing values (data initially lacking or removed via quality checks).     

Estimation of monthly-mean daily global solar radiation based on MODIS and TRMM products

Global solar radiation (GSR) is required in a large number of fields. Many parameterization schemes are developed to estimate it using routinely measured meteorological variables, since GSR is directly measured at a limited number of stations. Even so, meteorological stations are sparse, especially, in remote areas. Satellite signals (radiance at the top of atmosphere in most cases) can be used to estimate continuous GSR in space. However, many existing remote sensing products have a relatively coarse spatial resolution and these inversion algorithms are too complicated to be mastered by experts in other research fields. In this study, the artificial neural network (ANN) is utilized to build the mathematical relationship between measured monthly-mean daily GSR and several high-level remote sensing products available for the public, including Moderate Resolution Imaging Spectroradiometer (MODIS) monthly averaged land surface temperature (LST), the number of days in which the LST retrieval is performed in 1 month, MODIS enhanced vegetation index, Tropical Rainfall Measuring Mission satellite (TRMM) monthly precipitation. After training, GSR estimates from this ANN are verified against ground measurements at 12 radiation stations. Then, comparisons are performed among three GSR estimates, including the one presented in this study, a surface data-based estimate, and a remote sensing product by Japan Aerospace Exploration Agency (JAXA). Validation results indicate that the ANN-based method presented in this study can estimate monthly-mean daily GSR at a spatial resolution of about 5 km with high accuracy.     

Developing regional calibration coefficients for estimation of hourly global solar radiation in Ireland

This study proposed regional coefficients for estimating hourly global solar radiation through the adaptation of some empirical models that relate radiation to climatological and geographical variables. A total of 10 models were adapted over 7 stations in Ireland. The performance of the models was evaluated using some selected error indicators including the global performance index which combines all other error indices. The results indicated that the sunshine based regional calibration coefficients generated through a polynomial approach was most superior over other models with the lowest RMSE (0.2–0.3?MJm^?2?hr^?1), MAE (0.1–0.2?MJm^?2?hr^?1) and Pbias (0–7.0%) and highest R² and KGE (>0.85). The study found no local effect such as instrumental siting, observational uncertainty and climate on the variations of these coefficients. This outcome will therefore facilitate the design of various local and/or regional solar energy applications at microscale in a temperate region.     

On a universal model for the prediction of the daily global solar radiation

A model to predict the mean expected daily global solar radiation, H(n) on a day n, at a site with latitude φ is proposed. The model is based on two cosine functions. A regression analysis taking into account the mean measured values H_m.meas(n) obtained from SoDa database for 42 sites in the Northern Hemisphere resulted in a set of mathematical expressions of split form to predict H(n). The parameters of the two cosine model for 0°<φ < 23° are obtained by regression analysis using a sum of 3–8 Gaussian functions, while for 23°<φ < 71° the two cosine model parameters are expressed by a sum of exponential functions or the product of an exponential and a cosine function. The main equation of the model and the set of parametric expressions provide H(n) for any φ on Earth. Validation results of this model are provided along with the statistical estimators NMBE, NRMSE and t-statistic in comparison to the corresponding values from three databases of NASA, SoDa and the measured values from ground stations provided in Meteonorm.     

The assessment of different models to predict the global solar radiation on a surface tilted to the south

Global and diffuse solar radiation intensities are, in general, measured on horizontal surfaces, whereas stationary solar conversion systems (both flat plate solar collector and PV) are tilted towards the sun in order to maximize the amount of solar radiation incident on the collector surface. Consequently, the solar radiation incident on a tilted surface must be determined by converting the solar radiation intensities measured on a horizontal surface to that incident on the tilted surface of interest. There exist a large number of models designed to perform such a conversion. 11 such models have been tested utilizing data measured in Beer Sheva, Israel. The data consist of hourly global and diffuse solar radiation on a horizontal surface, normal incidence beam and global radiation on a south-oriented surface tilted at 40°. The horizontal diffuse radiation measured using a shadow ring was corrected using four different correction models. This resulted in 44 model permutations. The individual model performance is assessed by an inter-comparison between the calculated and measured solar global radiation on the south-oriented surface tilted at 40° using both graphical and statistical methods. The relative performance of the different models under different sky conditions has been studied. Different grading systems have been applied in an attempt to score the relative performance of the models.     

Analysis of experimental data on diffuse solar radiation in Athens,Greece, for building applications

Proper design and performance prediction of solar energy systems requires accurate information on the availability of solar radiation. The diffuse-to-total radiation correlation, originally developed by Liu and Jordan, has been extensively used as the technique and provided excellent results, although it was latitude dependent and not universally applicable. Thus, diffuse fraction correlations of this type have been developed by few other authors and for different location. This paper presents an analysis of hourly diffuse radiation on a horizontal surface. Hourly pyranometer data from Athens, Greece, are used to establish relationships between the diffuse fraction and the clearness index k _T for hourly and daily values. The results of the proposed equations are then compared with earlier equations. For the urban conditions of Athens the developed correlation fit to the empirical data.     

Generation of synthetic daily global solar radiation data based on ERA-Interim reanalysis and artificial neural networks

Four variables (total cloud cover, skin temperature, total column water vapour and total column ozone) from meteorological reanalysis were used to generate synthetic daily global solar radiation via artificial neural network (ANN) techniques. The goal of our study was to predict solar radiation values in locations without ground measurements, by using the reanalysis data as an alternative to the use of satellite imagery. The model was validated in Andalusia (Spain), using measured data for nine years from 83 ground stations spread over the region. The geographical location (latitude, longitude), the day of the year, the daily clear sky global radiation, and the four meteorological variables were used as input data, while the daily global solar radiation was the only output of the ANN. Sixty five ground stations were used as training dataset and eighteen stations as independent dataset. The optimum network architecture yielded a root mean square error of 16.4% and a correlation coefficient of 94% for the testing stations. Furthermore, we have successfully tested the forecasting capability of the model with measured radiation values at a later time. These results demonstrate the generalization capability of this approach over unseen data and its ability to produce accurate estimates and forecasts.     

Artificial neural network estimation of global solar radiation using air temperature and relative humidity

Measured air temperature and relative humidity values between 1998 and 2002 for Abha city in Saudi Arabia were used for the estimation of global solar radiation (GSR) in future time domain using artificial neural network method. The estimations of GSR were made using three combinations of data sets namely: (i) day of the year and daily maximum air temperature as inputs and GSR as output, (ii) day of the year and daily mean air temperature as inputs and GSR as output and (iii) time day of the year, daily mean air temperature and relative humidity as inputs and GSR as output. The measured data between 1998 and 2001 were used for training the neural networks while the remaining 240 days’ data from 2002 as testing data. The testing data were not used in training the neural networks. Obtained results show that neural networks are well capable of estimating GSR from temperature and relative humidity. This can be used for estimating GSR for locations where only temperature and humidity data are available.