Monthly Global Solar Radiation Research Articles

Calibration of the Ångström–Prescott Model for Accurately Estimating Solar Radiation Spatial Distribution in Areas with Few Global Solar Radiation Stations: A Case Study of the China Tropical Zone

The Ångström–Prescott formula is commonly used in climatological calculation methods of solar radiation simulation. Aiming at the characteristics of a vast area, few meteorological stations, and uneven distribution in the tropical regions of China, in order to obtain the optimal parameters of the global solar radiation calculation model, this study proposes a suitable monthly global solar radiation model based on the single-station approach and the between-groups linkage of the A–P model, which utilizes monthly measured meteorological data from 80 meteorological stations spanning the period from 1996 to 2016 in the tropical zone of China, considering the similarity in changes of monthly sunshine percentage between stations. The applicability and accuracy of the correction parameters (a and b coefficients) were tested and evaluated, and then the modified parameters were extended to conventional meteorological stations through Thiessen polygons. Finally, the spatial distribution of solar radiation in the tropical region of China was simulated by kriging, IDW, and spline interpolation techniques. The results show the following: (1) The single-station model exhibited the highest accuracy in simulating the average annual global solar radiation, followed by the model based on the between-groups linkage. After optimizing the a and b coefficients, the simulation accuracy of the average annual global solar radiation increased by 5.3%, 8.1%, and 4.4% for the whole year, dry season, and wet season, respectively. (2) Through cross-validation, the most suitable spatial interpolation methods for the whole year, dry season, and wet season in the tropical zone of China were IDW, Kriging, and Spline, respectively. This research has positive implications for improving the accuracy of solar radiation prediction and guiding regional agricultural production.

Estimation of monthly global solar radiation over twelve major cities of Libya

This study aims to estimate monthly averaged daily horizontal global solar radiation. Measured climatological data collected at twelve major cities located across Libya's map were used to establish 7 different empirical models. The empirical coefficients of the models were calculated using the least square method. The accuracy of the models was evaluated using different statistical criteria such as Taylor diagram, mean absolute percentage error, MAPE, and root mean square error, RMSE. The results indicated that the sunshine duration-based models are more accurate than air temperature-based models, and the best performance was obtained by the quadratic regression model for all twelve Libyan cities. Moreover, this regression model can be used for the prediction of monthly mean horizontal global solar radiation at a specific site across Libya's regions with minimum error. Furthermore, the results of the global solar irradiance produced by this method can be used for designing solar systems applications.

Comparative Assessment of Angstrom Sunshine-Base Models for Estimation of Global Solar Radiation in Owerri, Nigeria

Comparative assessment of the average monthly global solar radiation on horizontal surface for Owerri has been carried out with the use of five Angstrom sunshine base models. Thirty years (1992 - 2022) of average monthly global solar radiation and sunshine hours are sourced from NASA and NIMET, respectively. The sunshine-base models are evaluated with the use of geographical and metrological data of Owerri and comparison is made based on estimated statistical error indices of Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), Coefficient of Determination (R2) and percentage error to assess the applicability accuracy of the models. Results obtained shows that regression equations of Fagbenle and Page models satisfied viability criterion of MBE, RMSE, and MPE being close to zero, the R2 values are reasonably close to unity and the percentage error agreed to within ± 10% of the measured average monthly global solar radiation on a horizontal surface in Owerri. Rietveld model closely followed with less prediction accuracy to those of Fagbenle and Page models, while, Glower and McCulloh and Turton models yield poor estimation of the average monthly global solar radiation on horizontal surface.

Comparative Valuation of Global Solar Radiation on Horizontal and Tilt Surfaces in Owerri, Nigeria

Comparative valuation of global solar radiation on horizontal and tilt surfaces with the use of Angstrom-Page and four isotropic models for Owerri has been carried out in this paper. Angstrom linear regression model is developed from thirty years (1992-2022) of sunshine hour and average monthly global solar radiation sourced from NIMET and NASA, respectively. The Angstrom regression coefficients 𝑎 and 𝑏determined for Owerri is 0.210 and 0.696, respectively. The developed model is used to estimate average monthly global solar radiation on horizontal surface and consequently, the amount of incident solar radiation received on tilt surface by the isotropic models. The MBE, RMSE and MPE values are estimated to be 0.0083, 0.0029 and -0.0041%, respectively, for the developed Angstrom-Page model. A strong coefficient of determination of 76.6% was obtained and Analysis of Variance (ANOVA) significant of 100% was observed, which shows the goodness of fitness of the model. Among the isentropic models studied, one of the models poorly estimated solar radiation on horizontal and tilt surfaces for the study location

A Comparative Assessment of Time Series Forecasting Using NARX and SARIMA to Predict Hourly, Daily, and Monthly Global Solar Radiation Based on Short-Term Dataset

Several hourly, daily and monthly global solar radiation prediction models have been designed, to overcome the weakness of the previous models. Many previous models have used a long-term global solar radiation of Seoul to predict the consecutive years. Unfortunately, many countries do not have an enough history to build such prediction models, in addition many researchers suggested that seasonal autoregressive integrated moving average (SARIMA) is better than nonlinear autoregressive exogenous (NARX) neural network in predicting global solar radiation. Therefore, this research comes to fill the gaps in previous work, develop prediction model based on short-term global solar radiation, and test the best model between NARX and SARIMA by using global solar radiation of Seoul. The methodology divided the developed models into two parts including train phase and test phase. Train phase used dataset between 2007 and 2013, where test phase used dataset between 2014 and 2015. Afterward, the developed models are validated and tested using determination coefficient (R2) and different error function and the results are compared to two previous model that used long-term dataset namely ANFIS model and SARIMA. The results showed that the determination coefficient (R2) and RMSE of NARX model based on hourly data are 0.95 and 0.23 MJ/m2, respectively, besides the best daily and monthly average solar radiation predictors are obtained when NARX and hourly data are used. The results revealed that using hour, day, month and year as independent variables and less history with NARX model is efficient to predict two consecutive years.

The Prediction of Solar Radiation for Five Meteorological Stations in Libya Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

The prediction of solar radiation is very important tool in climatology, hydrology and energy applications, as it permits estimating solar data for locations where measurements are not available. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is presented to predict the monthly global solar radiation on a horizontal surface in Libya. The real meteorological solar radiation data from 5 stations for the period of 1982 - 2009 with diffrent latitudes and longitudes were used in the current study. The data set is divided into two subsets; the fist is used for training and the latter is used for testing the model. (ANFIS) combines fuzzy logic and neural network techniques that are used in order to gain more effiency. The statistical performance parameters such as root mean square error (RMSE), mean absolute percentage error (MAPE) and the coeffient of effiency (E) were calculated to check the adequacy of the model. On the basis of coeffient of effiency, as well as the scatter diagrams and the error modes, the predicted results indicate that the neuro-fuzzy model gives reasonable results: accuracy of about 92% - 96% and the RMSE ranges between 0.22 - 0.35 kW.hr/m2/day

Empirical models for estimating the global solar radiation of Jubail Industrial City, the Kingdom of Saudi Arabia

Empirical models provide precise and cost-effective information on solar energy assessment. The main objective of this work is to create and examine some regression models (linear, quadratic and logarithmic), for the estimation of the monthly global solar radiation potential in the Jubail Industrial City of Saudi Arabia. The industrial city has more solar energy potential, and the global horizontal irradiance is ranging from 4000 to 7000 Wh/m2/day. In order to initiate the green energy development in the city, site-specific hybrid models, which includes the ambient temperature, sunshine hours and relative humidity have been developed. Precise equation for the three proposed models is built up and examined for the industrial city. All the three empirical models provide good results. The statistical error estimation of the developed models reveals that the estimated and measured values appear exceptionally great and deliver similar outcomes. The values of the correlation coefficient and coefficient of determination of the quadratic model are 0.939 and 0.925, respectively, which are the highest among the three models. The mean absolute percentage error of the quadratic model is only 6.17%, and the other statistical errors are also comparatively low for the quadratic model. Therefore, the quadratic model gives an improved approximation on the estimation of solar energy parameters in which the errors between the measured and calculated values are smaller. Thus, the present regression models provide valuable data on the solar strength probable in the industrial city, and the data could be used for future energy planning.

Predictive model and assessment of the potential for wind and solar power in Rayak region, Lebanon

With the increasing consumption of fossil fuel, reducing greenhouse gas (GHG) emissions has become a serious issue that has attracted worldwide attention. Therefore, Lebanon is currently interested in utilizing renewable energy technologies to reduce energy dependence on oil reserves and GHG emissions. The present study is focused on solar and wind power potential and the economic viability of wind/solar systems for the Rayak region in Lebanon for the first time. The input data sources for the study including the Meteorological data for wind speed and NASA database for solar energy. In the assessment of wind energy, a two-parameter Weibull distribution function was used to analyze the characteristics of wind speed. Yearly and seasonal Weibull parameters were calculated for 10 m height using the Maximum likelihood method. In addition, yearly and seasonal wind power density values were calculated. The results showed that the mean annual wind speed and wind power density values were 5.884 m/s and 124.534 W/m2, respectively, during the investigation period. It can be concluded that the value of wind power density at the region was classified as marginal wind power potential and high-scale wind turbines can be used to gather wind energy potential in the region. Furthermore, predicting wind speed depends on various atmospheric factors and random variables. Therefore, 63 ANN models are developed by varying the meteorological parameters to predict the daily wind speed in the selected region. All the models with various combinations are validated and the performances of the models are analyzed using root mean squared error. The results demonstrated that the most relevant input variables for predicting the daily wind speed were found to be temperature, pressure, and relative humidity. In the assessment of wind energy, average monthly global solar radiation data were evaluated. It is found that the annual global solar radiation was 1877.41 kWh/m2, which indicates the selected region has high solar resources and is categorized as an excellent potential class. Moreover, this study provides a comprehensive and integrated feasibility analysis of 100 MW grid-connected wind and solar projects economic projects that can be developed in the country to reduce the electricity crisis and GHG emissions. Several different economic and financial indicators were calculated. The results indicate that the wind farm is a more economical option than the solar plant because of the higher values of NPV, BCR, ALCS, and IRR as well as the lower values of EB, SB, and LCOE.

Solar Energy Technology for Northern Cyprus: Assessment, Statistical Analysis, and Feasibility Study

Solar power is the fastest-growing energy source in the world. New technologies can help to generate more power from solar energy. The present paper aims to encourage people and the government to develop solar energy-based power projects to achieve sustainable energy infrastructures, especially in developing countries. In addition, this paper presents a solar energy road map to attract investors to invest in clean energy technology to help reduce the effect of global warming and enhance sustainable technological development. Therefore, the first objective of the paper is to analyze and compare the monthly global solar radiation for five different locations in Northern Cyprus using the measured data collected from the Meteorological Department and estimated values collected from the satellite imagery database. In addition, the mean hourly meteorological parameters including global solar radiation, air temperature, sunshine, and relative humidity are analyzed statistically and the type of distribution functions are selected based on skewness and kurtosis values. Accordingly, estimating global solar radiation improves solar power generation planning and reduces the cost of measuring. Therefore, models of a surface were analyzed by means of polynomial adjustments considering the values of R-squared. Finally, this study provides a comprehensive and integrated feasibility analysis of a 100 MW grid-connected solar plant project as an economic project in the selected region to reduce electricity tariffs and greenhouse gas (GHG) emissions. RETScreen Expert software was used to conduct the feasibility analysis in terms of energy production, GHG emissions, and financial parameters for the best location for the installation of a 100 MW grid-connected photovoltaic (PV) plant. Finally, the results concluded that the proposed solar system could be used for power generation in Northern Cyprus.

Estimation of the Monthly Global, Direct, and Diffuse Solar Radiation in Japan Using Artificial Neural Network

Estimation of the Monthly Global, Direct, and Diffuse Solar Radiation in Japan Using Artificial Neural Network

Global solar radiation prediction for Makurdi, Nigeria, using neural networks ensemble

ABSTRACT In this work, various artificial neural networks (ANNs) used for predicting the solar radiation in Makurdi city, Nigeria (7°7ʹ N long. 8°6ʹ) have been developed: Feed-forward back-propagation neural network (FFNN), radial basis function network (RBFN), and generalized regression neural network (GRNN). The main objective of this study is the use of an ANN’s ensemble for the prediction of average monthly global solar radiation of Makurdi in order to improve the prediction accuracy. The training and testing data were obtained from the Nigeria metrological station (NIMET) Makurdi. For each neural network type, average R 2 = 0.998 and MSE = 0.0142, performance measures were obtained when the networks were analyzed. In order to improve prediction accuracy, an ensemble of neural networks was examined which gave an R 2 = 1.0 and MSE = 0.0139. All the proposed neural networks predicted solar radiation with great accuracy; nevertheless, ensemble achieved better results implying high dependency of the model for the evaluation of solar radiation in the locations where solar radiation instruments are not available or faulty.

Wavelet-based 3-phase hybrid SVR model trained with satellite-derived predictors, particle swarm optimization and maximum overlap discrete wavelet transform for solar radiation prediction

The accurate prediction of global solar radiation (GSR) with remote sensing in metropolitan, regional and remote, yet solar-rich sites, is a core requisite for cleaner energy utilization, monitoring and conversion of renewable energy into usable power. Data-driven models that investigate the feasibility of solar-fueled energies, face challenges in respect to identifying their appropriate input data as such variables may not be available at all sites due to a lack of environmental monitoring system. In this paper, the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite-derived predictors are employed to train three-phase hybrid SVR model for monthly GSR prediction. Firstly, to acquire relevant model input features, MODIS variables are screened with the Particle Swarm Optimization (PSO) algorithm, and secondly, a Gaussian emulation method of sensitivity analysis is incorporated on all screened variables to ascertain their relative role in predicting GSR. To address pertinent issues of non-stationarities, PSO selected variables are decomposed with Maximum Overlap Discrete Wavelet Transformation prior to its incorporation in Support Vector Regression (SVR), constructing a three-phase PSO-W-SVR hybrid model where the hyper-parameters are acquired by evolutionary (i.e., PSO & Genetic Algorithm) and Grid Search methods. Three-phase PSO-W-SVR hybrid model is benchmarked with alternative machine learning models. Thirty-nine model scenarios are formulated: 13 without feature selection (e.g., SVR), 13 with feature selection (e.g., PSO-SVR for two-phase models) and the remainder 13 with feature selection strategy coupled with data decomposition algorithm (e.g., PSO-W-SVR leading to a three-phase model). Metrics such as skill score (RMSESS), root mean square error (RMSE), mean absolute error (MAE), Willmott’s (WI), Legates & McCabe’s (E1) and Nash–Sutcliffe coefficients (ENS) are applied to comprehensively evaluate prescribed models. Empirical results register high performance of three-phase hybrid PSO-W-SVR models, exceeding the prescribed alternative models. High predictive ability evidenced by a low RRMSE and high E1 ascertains PSO-W-SVR hybrid model as considerably favorable in its capability to be enriched by MODIS satellite-derived variables. Maximum Overlap Discrete Wavelet Transform algorithm is also seen to provide resolved patterns in satellite variables, leading to a superior performance compared to the other data-driven model. The research avers that a three-phase hybrid PSO-W-SVR model can be a viable tool to predict GSR using satellite derived data as predictors, and is particularly useful for exploration of renewable energies where satellite footprint are present but regular environmental monitoring systems may be absent.

Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction

Solar energy predictive models designed to emulate the long-term (e.g., monthly) global solar radiation (GSR) trained with satellite-derived predictors can be employed as decision tenets in the exploration, installation and management of solar energy production systems in remote and inaccessible solar-powered sites. In spite of a plethora of models designed for GSR prediction, deep learning, representing a state-of-the-art intelligent tool, remains an attractive approach for renewable energy exploration, monitoring and forecasting. In this paper, algorithms based on deep belief networks and deep neural networks are designed to predict long-term GSR. Deep learning algorithms trained with publicly-accessible Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data are tested in Australia’s solar cities to predict the monthly GSR: single hidden layer and ensemble models. The monthly-scale MODIS-derived predictors (2003–2018) are adopted, with 15 diverse feature selection approaches including a Gaussian Emulation Machine for sensitivity analysis used to select optimal MODIS-predictor variables to simulate GSR against ground-truth values. Several statistical score metrics are adopted to comprehensively verify surface GSR simulations to ascertain the practicality of deep belief and deep neural networks. In the testing phase, deep learning models generate significantly lower absolute percentage bias (≤3%) and high Kling–Gupta efficiency (≥97.5%) values compared to the single hidden layer and ensemble model. This study ascertains that the optimal MODIS input variables employed in GSR prediction for solar energy applications can be relatively different for diverse sites, advocating a need for feature selection prior to the modelling of GSR. The proposed deep learning approach can be adopted to identify solar energy potential proactively in locations where it is impossible to install an environmental monitoring data acquisition instrument. Hence, MODIS and other related satellite-derived predictors can be incorporated for solar energy prediction as a strategy for long-term renewable energy exploration.

Estimating the global solar radiation in Putrajaya using the Angstrom-Prescott model

Global solar radiation (GSR) studies are essential in enhancing the use of solar PV systems especially for Building Integrated PV system (BIPV). Various studies on GSR had been carried out in Malaysia including Penang, Kuala Lumpur and Kota Bharu at the earlier stage. Afterward, data from the Malaysian Meteorological Department (MET) and the Malaysian National University were used to estimate the monthly average daily global radiation for various locations in Malaysia including Kuala Lumpur, Kuala Lumpur, Petaling Jaya, Bandar Baru Bangi, Kota Baru, Senai, Bayan Lepas, Kuching and Kota Kinabalu. GSR in Putrajaya, a location which has identified to implement Malaysian Building Integrated Photovoltaic (MBIPV) projects has yet to be estimated. The conventional method to obtain the amount of GSR at a location is to install a measuring device like a pyranometer at the site to observe and document its day-to-day recordings. However, this is a time-consuming and costly practice. The models used to estimate the GSR are usually based on astronomical factors, geometrical factors, physical factors and meteorological factors. There are numerous models used to predict GSR which can be categorized based on sunshine, cloud, temperature and other meteorological parameters. Additionally, the global solar energy models can be grouped by its modelling techniques such as linear, nonlinear, artificial intelligence and fuzzy logic modelling techniques. The reviews discovered that the models used to estimate GSR were mostly correlated to sunshine duration (SD), air temperature, relative humidity and geographical parameters like latitude, longitude and altitude. In this paper, the GSR in Putrajaya is estimated from the hourly solar radiation and daily sunshine hours data from the Malaysian Meteorological Department. The data, along with the calculated monthly average daily extraterrestrial radiation on a horizontal surface and monthly average maximum possible daily sunshine is used to plot a scatter graph that is linearly fitted. The gradient and vertical axis cut-off from the linear fit is used to obtain the coefficients in the Angstrom-Prescott (A-P) model. Subsequently, the coefficients were used to estimate the GSR in Putrajaya. In line with the government’s initiative, this study will provide an estimation of the GSR in Putrajaya where the finding will be beneficial to the stakeholders, policy maker, developer and investor and energy authority. The average monthly GSR was obtained for nine out of twelve months with highest at 21.18 MJ/m2 in March and lowest at 18.34 MJ/m2 in June.

Estimation of global solar radiation, sunshine hour distribution and clearness index in Enugu, Nigeria

The concept of solar energy and its applications in this present day world would come to be one of the solutions to our present Nigerian problem of instability and epileptic power supply. In the study, the baseline data for mean monthly sunshine hours ( S ) and global solar radiation ( H ) for Enugu (6.7 0 N, 7.6 0 E) were obtained from the Nigeria Metrological Agency (NIMET), Oshodi Lagos Nigeria and spanned 1996 to 2010. The data for global solar radiation were measured with a Gunn-Bellani radiometer. The average sunshine hour for the period of study was estimated to be 5.67 hours; the Angstrom constants a and b of Angstrom-type correlation used in estimating monthly average global solar radiation was estimated to be 0.27 and 0.58 respectively; a linear regression correlation model was developed for Enugu. The result of global solar radiation was found to be 14.44 MJm -2 day -1 thereafter subjected to statistical tests [ MBE , RMSE , MPE ] and proved to be good estimates with previous literatures reviewed. The value of clearness index ( K T ) was estimated to range from 0.43 to 0.61 showing Enugu as so a partly clear sky city. Keywords: Global solar radiation, Sunshine hour distribution, Clearness Index Enugu

Estimation of Global Solar Radiation and Clearness Index in Coast of Gulf of Guinea, Nigeria

The concept of solar energy and its applications in present day would come to be one of the solutions to our Nigerian problem of instability and epileptic power supply. In this study, data for mean monthly sunshine hours and global solar radiation for Ikeja, Lagos state capital (6.580N, 3.320E) were obtained from Nigeria Metrological Agency (NiMeT), Oshodi Lagos, Nigeria and spanned 1996 to 2010. The data for global solar radiation were measured using a Gunn-Bellani radiometer. A linear regression correlation model was developed for Ikeja and other surrounding area in south-western part of Nigeria with similar meteorological conditions. The results of estimated global solar radiation ranged from 5.1 MJm-2 day-1 on average for August and 13.1 MJm-2 day-1 for March for Lagos. The Angstrom constants a and b of Angstrom-type correlation to estimate monthly average global solar radiation was estimated to be 0.25 and 0.63 respectively. The result for global solar radiation were then subjected to statistical tests [MBE, RMSE, MPE] and proved to be good estimates. The value of clearness index was also estimated to range from 0.31 to 0.59 showing Lagos as a partly clear sky city.

Assessment of global solar radiation to examine the best locations to install a PV system in Tunisia

The study of the solar radiation is the starting point of any investigation for a new energy, to study and search the best location to install a PV system. A very important factor in the assessment of solar potential is the availability of data for global solar radiation that must be coherent and of high quality. In this paper, we analyze the estimation result of the monthly global solar radiation for three different locations, Bizerte in Northern Tunisia, Kairouan in Middle Eastern Tunisia, and Tozeur in Southern Tunisia, measured on the surface by the National Institute of Meteorology and the meteorological year irradiation based on satellite imagery result PVGIS radiation databases. To get the right measurements with minimum error, we propose a numerical model used to calculate the global solar radiation in the indicated three sites. The results show that the model can estimate the global solar radiation (kWh/m²) at a specific station and over most area of Tunisia. The model gives a good estimation for solar radiation where error between the measured values and those calculated are negligible.

Comparative study of the monthly global solar radiation estimation data in Jakarta

The objective of this paper is to get the most suitable model of monthly global solar radiation estimation in Jakarta which performs better than the others model. In this study, measured data of solar radiation were utilized to be compared with the empirical models from the literature and a new based-temperature model, to estimate the monthly intensities of global solar radiation in Jakarta, Indonesia (Latittude: 6.13° S, Longitude: 106.8° E). Global solar radiations were measured for two complete years, and the estimation of monthly solar radiation were calulated by four empirical models and also one linear regression model. The accuracy of the models was then analyzed in terms of some statistical indicators such as Mean Bias Error (MBE) and Root Mean Square Error (RMSE). The statistical analysis of result revealed that the empirical models have not the same accuracy, and it is concluded that there are the models that can be preferred for the estimation of monthly global solar radiation in Jakarta, Indonesia. The obtained results indicate that Allen is performing better than the others by obtaining maximum MBE of 0.52 and maximum RMSE of 1.82. It also can be said that Allen is the most suitable model for estimating a monthly global solar radiation in Jakarta, Indonesia.

Time dependent prediction of monthly global solar radiation and sunshine duration using exponentially weighted moving average in southeastern of Turkey

This paper proposes a new approach for prediction of global solar radiation and sunshine duration based on earlier years of data for the eastern region of Turkey which has a high potential of solar energy. The proposed method predicts the basic parameters using time series and an analysis method. This method is exponentially weighted moving average. This model estimates next years global solar radiation and sunshine duration and is evaluated by statistical parameters, mean absolute percentage error (MAPE) and coefficient of determination, to examine the success of the proposed technique. In our study, the result shows that this method is effective in predicting global solar radiation and sunshine duration as regards of MAPE and coefficient of determination. The calculated MAPE which are between 0-10 kWh/m2 per day were assumed excellent and coefficient of determination were found significant per every year.

Smart model for accurate estimation of solar radiation

Prediction of solar radiation has drawn increasing attention in the recent years. This is because of the lack of solar radiation measurement stations. In the present work, 14 solar radiation models have been used to assess monthly global solar radiation on a horizontal surface as function of three parameters: extraterrestrial solar irradiance (G0), duration sunshine (S) and daylight hours (S0). Since it has been observed that each model is adequate for some months of the year, one model cannot be used for the prediction of the whole year. Therefore, a smart hybrid system is proposed which selects, based on the intelligent rules, the most suitable prediction model of the 14 models listed in this study. For the test and evaluation of the proposed models, Tamanrasset city, which is located in the south of Algeria, is selected for this study. The meteorological data sets of five years (2000–2004) have been collected from the Algerian National Office of Meteorology (NOM), and two spatial databases. The results indicate that the new hybrid model is capable of predicting the monthly global solar radiation, which offers an excellent measuring accuracy of R2 values ranging from 93% to 97% in this location.