Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction

Solar energy serves as a great alternative to fossil fuels as they are clean and renewable energy. Accurate solar radiation (SR) prediction can substantially lower down the impact cost pertaining to the development of solar energy. Lately, many SR forecasting system has been developed such as support vector machine, autoregressive moving average and artificial neural network (ANN). This paper presents a comprehensive study on the meteorological data and types of backpropagation (BP) algorithms used to train and develop the best SR predicting ANN model. The meteorological data, which includes temperature, relative humidity and wind speed are collected from a meteorological station from Kuala Terrenganu, Malaysia. Three different BP algorithms are employed into training the model i.e., Levenberg–Marquardt, Scaled Conjugate Gradient and Bayesian Regularization (BR). This paper presents a comparison study to select the best combination of meteorological data and BP algorithm which can develop the ANN model with the best predictive ability. The findings from this study shows that temperature and relative humidity both have high correlation with SR whereas wind temperature has little influence over SR. The results also showed that BR algorithm trained ANN models with maximum R of 0.8113 and minimum RMSE of 0.2581, outperform other algorithm trained models, as indicated by the performance score of the respective models.

Literature review. Sözen, et al. 19 conducted a study on the forecast of solar potential in Turkey using neural network approach. The main objective of this study is to put forward to solar energy potential in Turkey using ANNs with the following back propagation algorithms: scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg-Marquardt (LM) learning algorithms and logistic sigmoid transfer function. The inputs and outputs are normalized in the range of −1 to 1 and the ANN models are developed under MAT-LAB environment. The results obtained in terms of maximum mean absolute percentage error (MAPE) and absolute fraction of variance (R 2 ) were also compared with other classical regression models to predict solar radiation. The results of validation and comparative study indicate that ANN based prediction model has the advantage as compared to those classical regression models.
Kisi and Uncuoğlu 20 carried out a study on the performances of three BP algorithms, namely the LM, CG and RB for stream flow forecasting and determination of lateral stress in cohesionless soils. The study results showed that despite LM being the fastest and best performed algorithm (short training time and fast convergence speed) as compared to others in the training dataset, the RB algorithm was in fact the better algorithm in terms of accuracy for the testing dataset.
Following in the year of 2009, a study on the modelling of solar energy potential in Nigeria using ANN model by Fadare 11 was carried out. In this study, standard multi-layered, feed-forward, back-propagation neural networks with different architecture were designed using the neural toolbox for MATLAB. The data used to train and validate the model were the geographical and meteorological data of 195 cities in Nigeria obtained from the NASA geo-satellite database. The results from this study showed that the correlation coefficients between the ANN predictions and the actual mean monthly global solar radiation were over 90%, thus indicating a high reliability of the model for evaluation of solar radiation. A graphical user interface (GUI) was also developed for the application of the model.
In research carried out by Xinxing, et al. 21 , they have categorized BP algorithm into 6 classes as adaptive momentum, self-adaptive learning rate, resilient backpropagation, conjugate gradient, quasi-newton, bayesian regularization. In this study, the performance of these algorithms is being evaluated in terms of their predictive ability, convergence speed and training duration based on an electricity load forecasting model. From this study, it is found that BR algorithms have a fairly low MAPE at 3.5% as compared to other training algorithms. However, this high performance maybe due to its heavy processing load, hence slower training time. Recommendations have been made where the processing ability is limited, resilient backpropagation or conjugate gradient may be employed to reduce the training duration and achieve a rather accurate result. www.nature.com/scientificreports/ Mishra, et al. 22 has carried out a study on the analysis of LM and SCG training algorithm using a MLP based ANN to estimate channel equalizers. The performance of the algorithms is evaluated based on least square (LS) and minimum mean square error (MMSE). From the study results, the predictive ability and training speed of both algorithms are analogous. However, in the context of MSE against Epoch graph, the LM does have better accuracy compared to SCG. This is due to a relatively smaller dataset and hence the LM outperformed SCG algorithm on a simple MLP structure.
Subsequently, in the year of 2016, a more detailed study on the prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms by Premalatha and Valan Arasu 23 further proved the ability of ANN models to predict solar radiation to a certain accuracy. In this research, two ANN models with four different algorithms are considered. The ANN models are evaluate based on the minimum mean absolute error (MAE) and root mean square error (RMSE) and maximum linear correlation coefficient (R) of their respective results. The objective of this study is to compare the 4 back propagation algorithms: gradient descent (GD), Levenberg-Marquardt (LM), resilient propagation (RP) and scaled conjugate gradient (SCG). The input parameters used in this study are latitude, longitude, altitude, year, month, mean ambient air temperature, mean station level pressure, mean wind speed and mean relative humidity. The output is the monthly average global solar radiation. The results show that the ANN model with the LM algorithm achieved minimum values of MAE and RMSE. It is also shown that the LM algorithm is able to converge well within a shorter period of time among the four algorithms used to provide an accurate solution with minimum error.
In the same year, Kayri 24 conducted a study on the predictive ability of Bayesian Regularization and Levenberg-Marquardt algorithms in ANN based on a comparative empirical study on social data. The ANN model was tested with 1 to 5 neuron architectures respectively through MATLAB. From the results, it is concluded that the BR algorithm has a better performance compared to LM due to a higher correlation coefficient and lower SSE in terms of its predictive ability. Nevertheless, similar to the results from the study carried out by Kisi and Uncuoğlu 20 , the LM algorithm once again proved to be the algorithm with the fastest convergence due to a low MSE, it was still outperformed by the BR in terms of accuracy and predictive ability. Similarly, Okut, et al. 25 also carried out an investigation on the predictive performance of BR and SCG algorithms. In their study, it is found that BRANN had a better performance but not significantly so.
Ghazvinian, et al. 26 attempted to predict solar radiation by developing an integrated support vector regression and an improved particle swarm optimization-based model. A new prediction model for solar radiation based on support vector regression (SVR) is developed behind an improved particle swarm optimization (IPSO) algorithm. Different prediction models such as the M5 tree model (M5T), genetic programming (GP) and SVR integrated with different optimization algorithms e.g. SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model were tested along with different input parameters. This study showed that the SVR-IPSO model is superior as compared to other presented models. The performance of the model can be further enhanced by adding other input variables that directly influence solar radiation.
Artificial neural networks (ANNs) are one of the most essential components of soft computing. They are used to replicate the functioning of the human brain and to analyse and process data. The ability of ANNs to self-learn allows them to calculate accurate responses to problems that are difficult to solve using traditional analytical methods. It can comprehend, ask, and learn without having to be reprogrammed, grasp missing data, be easily preserved, have high accuracy, be implemented on parallel hardware, and respond to nonlinear complicated models without imposing any limitations or assumptions on the incoming data. Because of their resilience and efficacy, neural network-based algorithms and stochastic methods have recently received a lot of interest in the fields of computer science, engineering, and ANN. The ANN has been widely used in different research areas and help solving complicated problems. In this context, through a Bayesian Regularization approach based on neural networks, physical parameters such as thermal relaxation parameter, prandtl number, fluid suction/injection, and stretching/shrinking sheet have been successfully computed as reported in 27 . In addition 28 , concluded that by varying surface thickness using trained Artificial Neural Networks and the Levenberg-Marquardt Back-propagation (ANNLMB) procedure, the strength of Back-propagated Intelligent Networks (BINs) is manipulated and showed outstanding performance for numerical investigations of randomness attributes in magnetohydrodynamics (MHD) nanofluidic flow model. Different dimension of ANN application has been developed to the second kind of Three-point singular boundary value problems (TPS-BVPs) by 29 . In this study, several enhancements to the ANN has been proposed utilizing different optimization techniques and algorithms to achieve better results and showed that the ANN modelling approach could solve such complex application. Furthermore, for addressing the HIV infection model of CD4 + T cells 30 , developed an integrated intelligent computing framework that used a layered structure of neural network with diverse neurons and their optimization with efficacy of global search using genetic algorithms. The study showed that the proposed ANN modelling approach is robust, trustworthy and convergent 31 showed that the ANN could be successfully developed and implemented to solve the third-order nonlinear multiple singular systems represented with Emden-Fowler differential equation (EFDE).

Materials and methods
In this study, the data provided are the meteorological data of Kuala Terengganu, Malaysia obtained from the Malaysian Meteorological Department. The data collected are the 24-h mean temperature, 24-h mean relative humidity, 24-h mean wind speed and global radiation from year 1985 to 2012. Besides, the latitude, longitude and elevation of the Kuala Terengganu meteorological station, covering the largest city in the area 32 , were also given at 5° 23′ N, 103° 06′ E and 5.2 m respectively. The meteorological data collected are solely based on one meteorological station so this may pose a problem of the data being less diversified as the climatic condition is pretty constant. Malaysia is located near to the equatorial line, hence the tropical rainforest climate with high rate www.nature.com/scientificreports/ of rainfall 33 and overall high temperature throughout the entire year 34 is observed at the location of our studies. Besides, the meteorological data at night may also be captured by the meteorological station, hence resulting at the zero values of global radiation. These extreme values may reduce the effectiveness of the learning ability of the ANN model, thus reducing its accuracy to predict solar radiation.
The methodology of the current study follows the steps represented in Fig. 1. The obtained data is processed accordingly to prepare for ANN models training. Based on this data optimum neurons are selected which provides better training accuracy. The processed data and the selected optimum neurons are then used to train the intended models. Best models are selected using statistical analysis and are then compared with different model developed in literature.

Preparation of data.
Before employing the data to create and train the ANN model, the data have to go through normalization. Data normalization is a very common technique that is applied to prepare the data for machine learning. The objective of normalization is to alter the numeric values in the dataset to use a common scale, without distorting differences in the ranges of values or losing information. By normalizing the data, we are able to create new values from within the data that maintain the general distribution and ratios in the source data, while keeping values within a scale applied across all numeric columns used in the model. The meteorological data (inputs) are normalized by transforming them into values within the range of −1 and 1 using basic coding in MATLAB. Besides, the GSR has also been put through a log transformation to ensure the data is not too skewed and approximate to normality. The formula of normalization and log transformation is also shown as below: www.nature.com/scientificreports/ where X is meteorological data (temperature, relative humidity, windspeed), X min is minimum value of all available meteorological data, X max is maximum value of all available meteorological data, X N = normalized meteorological data, Y T = log transformed output (solar radiation) and Y = actual output. A total of 8431 samples of meteorological data from Kuala Terengganu are randomly divided according to the ratio; training = 70%, validation = 15% and testing = 15%. This ratio is maintained throughout the development of ANN for all four models. After this, number of hidden neurons is set at 15 as we have determined in Sect. 2.2.

Selection of optimum number of hidden neurons.
Determining the optimum number of hidden neurons in the hidden layer can be a very complicated process. Having the optimum number of hidden neurons is able to ensure a great accuracy of the ANN model and achieve a minimum possible error in the output. In order to select the optimum number of hidden neurons in the hidden layer, model IV was developed by increasing the number of neurons one by one until it converged into the smallest mean squared error. Since the Levenberg-Marquardt algorithm is the one that produces the highest R value (in the range of 0.7 to 0.8) among the four algorithms, the LM algorithm is used in the selection of the optimum number of hidden neurons. As shown in Table 1 below, model IV is trained several times using different number of hidden neurons and the R values for both the training and testing is slowly increasing as the number of neurons is increased. When the hidden neuron is increased until 15, a maximum R value for both the training data and testing data is reached (0.8024 for training and 0.8231 for testing). This means that the predicted values show great correlation with the actual values and they are consistent with each other. When the number of hidden neurons is further increased up until 20, the R values are decreasing. Hence, the optimum number of hidden neurons in the hidden layer is determined to be 15 and shall be used in our present work. Development of ANN model. The architecture (Fig. 2) above demonstrate how does MATLAB train the network using the input data through a series of transfer and activate function, and subsequently being able to predict the output with minimum error. As seen from the figure above, the architecture of an ANN is made up of a series of individual component, which, in fact are represented in matrix form. The network above can be used as a general function approximator to approximate any function with a finite number of discontinuities.
The accuracy of the approximation depends on the number of neurons in the hidden layer. A few key parameters to take note in the architecture above is that N 1 represents the number of neurons in the input layer while M represents the number of elements in the input layer. The IW (input weight) matrix is a N 1 x M matrix whereas B 1 (bias vector) is a N 1 length matrix. This is similar for the components in the output layer, only to be differentiated by the superscript 1 & 2 beside the matrix, where 1 would mean the matrix is associated with the input layer and 2 means that the matrix is associated with the output layer. A 1 is the transformed matrix from the hidden layer that has undergone transformation through the tan-sig (tan-sigmoid) function. The tan-sig transfer function is a function that takes a matrix of net input vectors, S 1 and returns the matrix A 1 in the range of [−1, 1]. Similarly, A 2 is a transformed matrix from the purelin (linear) function. Purelin transfer function works similarly as the where X i = actual global solar radiation (MJm −2 ), Y i = predicted global solar radiation (MJm −2 ), N = total number of data, X i = actual global solar radiation (MJm −2 ), Y i = predicted global solar radiation (MJm −2 ), X = average of actual global solar radiation (MJm −2 ) and N = total number of data. In general, after running the script of the ANN, all the key information and data will be produced, such as the predicted output, the difference between the actual and predicted output, the performance of the model in terms of MSE. After running through the entire process for all four models with all three different backpropagation algorithms, all of the relevant parameters and key data are then recorded into an Excel sheet in order to carry out the appropriate evaluation on the models' performance.

Result and discussion
Model I (temperature and relative humidity). In Model I, two input parameters are considered here.
• 24-h mean relative humidity (%) • 24-h mean wind speed (m/s) The architecture of the neural network consisted of above-mentioned two input parameters, one hidden layer with 15 nodes in it and one output layer.
Levenberg-Marquardt. The results of Model I trained using the LM backpropagation algorithm is shown in Table 2.
100 sets of actual and predicted GSR are randomly selected and plotted to aid in the visualization of the correlation between the actual values and the predicted values by the ANN model (Fig. 3).
Scaled conjugate gradient. The results of Model I trained using the SCG backpropagation algorithm is shown in Table 3. Figure 2. Architecture of ANN. www.nature.com/scientificreports/ 100 sets of actual and predicted GSR are randomly selected and plotted to aid in the visualization of the correlation between the actual values and the predicted values by the ANN model (Fig. 4).
Bayesian regularization. The results of Model I trained using the BR backpropagation algorithm is shown in Table 4: 100 sets of actual and predicted GSR are randomly selected and plotted to aid in the visualization of the correlation between the actual values and the predicted values by the ANN model (Fig. 5).
Model II (temperature and windspeed). In Model II, two input parameters are considered here: Levenberg-Marquardt. The results of Model II trained using the LM backpropagation algorithm is shown in Table 5: 100 sets of actual and predicted GSR are randomly selected and plotted to aid in the visualization of the correlation between the actual values and the predicted values by the ANN model (Fig. 6).

Model I -Bayesian RegulariaƟon
Actual PredicƟon   Table 6. 100 sets of actual and predicted GSR are randomly selected and plotted to aid in the visualization of the correlation between the actual values and the predicted values by the ANN model (Fig. 7).
Bayesian regularization. The results of Model II trained using the BR backpropagation algorithm is shown in Table 7.
100 sets of actual and predicted GSR are randomly selected and plotted to aid in the visualization of the correlation between the actual values and the predicted values by the ANN model (Fig. 8).

Model III (windspeed and relative humidity).
In Model III, two input parameters are considered here:   The architecture of the neural network consisted of above-mentioned two input parameters, one hidden layer with 15 nodes in it and one output layer. Table 8.

Levenberg-Marquardt. The results of Model III trained using the LM backpropagation algorithm is shown in
100 sets of actual and predicted GSR are randomly selected and plotted to aid in the visualization of the correlation between the actual values and the predicted values by the ANN model (Fig. 9).
Scaled conjugate gradient. The results of Model III trained using the SCG backpropagation algorithm is shown in Table 9: 100 sets of actual and predicted GSR are randomly selected and plotted to aid in the visualization of the correlation between the actual values and the predicted values by the ANN model (Fig. 10).
Bayesian regularization. The results of Model III trained using the BR backpropagation algorithm is shown in Table 10: 100 sets of actual and predicted GSR are randomly selected and plotted to aid in the visualization of the correlation between the actual values and the predicted values by the ANN model (Fig. 11).

Model III -Scaled Conjugate Gradient
Actual PredicƟon  The architecture of the neural network consisted of above-mentioned three input parameters, one hidden layer with 15 nodes in it and one output layer.
Levenberg-Marquardt. The results of Model IV trained using the LM backpropagation algorithm is shown in Table 11:   www.nature.com/scientificreports/ 100 sets of actual and predicted GSR are randomly selected and plotted to aid in the visualization of the correlation between the actual values and the predicted values by the ANN model (Fig. 12).
Scaled conjugate gradient. The results of Model IV trained using the SCG backpropagation algorithm is shown in Table 12. 100 sets of actual and predicted GSR are randomly selected and plotted to aid in the visualization of the correlation between the actual values and the predicted values by the ANN model (Fig. 13).
Bayesian regularization. The results of Model IV trained using the BR backpropagation algorithm is shown in Table 13.
100 sets of actual and predicted GSR are randomly selected and plotted to aid in the visualization of the correlation between the actual values and the predicted values by the ANN model (Fig. 14).
All the models developed above have same internal configuration (i.e. same number of hidden layer(1) and neurons (15)), but all the models differ in their input variables combination and training algorithms. The results of all the models consists of statistical values (i.e. MAE, RMSE, MSE, R and NSE) and a plot of actual and predicted GSR. All the plots look similar to each other, yet there is slight change in the plot which is reflected in its statistical values. Further comparison of the statistical values to produce the best training algorithm and best model with best combination of inputs are done in following sections.

Selection of the best backpropagation algorithm.
In order to determine the most suitable algorithm to be used in our ANN model for solar prediction, results tabulated in Table 14 will be used. In this case, a low   Although the minimum MAE in Model III is LM's 0.1864, but the difference is insignificant when compared to BR's 0.1872, hence it is concluded that the BR algorithm has the best mean absolute error for all four models. Besides, BR algorithm also has the minimum RMSE values of 0.2884, 0.3065, 0.2698 and 0.2581 for Model I, Model II, Model III and Model IV respectively. By logic, BR algorithm will also obtain minimum MSE values for all four models since MSE is the square of RMSE. It can also be seen that BR algorithm has achieved maximum R values of 0.74377 and 0.78104 for Model II and Model III respectively. LM algorithm obtained the maximum R values for Model I and Model IV at 0.7566 and 0.8142, but the difference are again insignificant when compared to BR's 0.7565 and 0.8113. Also, BR algorithm has achieved highest NSE value in all the four models i.e., 0.5770, 0.5192, 0.6313 and 0.6654 for model I, II, III and IV, respectively.
The correlation coefficient is plotted and summarized in Fig. 15 for a clearer picture on the robustness of each BP algorithm.  www.nature.com/scientificreports/ Based on the results, it can be seen that the BR and LM trained ANN model are far superior when compared to the SCG trained model in terms of predictive ability and accuracy. This may be due to the SCG uses less memory in training the data as well as a faster training time (less average training iteration). However, when comparing the BR and LM algorithm, BR algorithm only has a slight edge over the LM algorithm as all of their key statistics are pretty close. This is due to the various advantages of the BR algorithm such as the robustness of the model, the probability distribution of network weights approach and optimization of the ANN architecture. This will help in overcoming overfitting issues due to a better data generalization ability. Hence, it can be said that the Bayesian Regularization BP algorithm is the best suited algorithm in our study to develop a SR predicting ANN model. Selection of the best ANN model. The comparison study above has proven BR algorithm to be the best suited algorithm to help train the ANN model. Now, a comparison between the models using the BR algorithm will be carried out to evaluate the meteorological data that are best used for SR prediction. The MAE, RMSE, R and NSE values of all four models are tabulated in Table 15 below.
As seen from Table 15 above, Model IV which has a combination of all the meteorological data as input is the best model as it has achieved minimum MAE, RMSE and maximum R and NSE at 0.1789, 0.2581, 0.8113 and 0.6654, respectively among the four models. All of these values appear to be better than the rest of the models which only used two of the three available meteorological data. Thus, it is concluded that if one wishes to achieve the greatest accuracy in SR prediction using ANN, mean temperature, mean relative humidity and mean wind speed should be used together as the input. Now among the remaining 3 models, it can be seen that model III has the minimum MAE, RMSE and maximum R and NSE at 0.1872, 0.2698, 0.78104 and 0.6313, respectively as compared to Model I and Model II. The conclusion obtained from this comparison is that the combination of mean relative humidity and mean temperature will develop the ANN model with the best performance. We are also able to conclude that mean wind speed has little influence over solar radiation.
Analysis of error. Based on the discussion above, it is found that BR algorithm is the best training algorithm and when employed in Model IV, the performance and accuracy of the predicted output is the best among the other models. This can be further justified by analysing the error of each models using mean absolute percentage error (MAPE). MAPE is a measure of prediction accuracy as a percentage and is commonly used to evaluate the accuracy of a forecast system, and in this case would be the ANN model. The formula of MAPE is given as below:  www.nature.com/scientificreports/ where x i = actual measured data, y i = predicted data and N = total number of data. Based on Table 16 above, it can be seen that the MAPE for all four models of different training algorithm are within the range of 10%-15%. The highest MAPE being 14.95%, which is from Model II's SCG. This result is expected as we have stated in Sect. 3.5 that the SCG algorithm performs fairly poor when compared to the other algorithms, hence resulting in a greater error in the ANN models that were trained using SCG. In general, the MAPE of all the models trained with the BR algorithm are fairly low when compared to the LM and SCG trained models.
The lowest MAPE in the Table 16 is found to be 10.64%, which is the MAPE of Model IV trained using the BR algorithm. Again, this is an anticipated scenario that Model IV will be the ideal ANN for solar prediction as it has taken into consideration all three meteorological parameters and was trained by the most suitable algorithm. Hence, it is no surprise that it has recorded the lowest MAPE among all the models. A 10.64% of MAPE would mean that on average, the solar radiation prediction from ANN Model IV is off by 10.64%.
Nevertheless, it is difficult to set an arbitrary performance target to determine what is a good MAPE only based on comparison of different models conducted in the same study. Hence, the MAPE of other similar studies has been brought in to make a thorough comparison and evaluation.
As seen from Table 17, the MAPE of our present work (10.64%) is ranked at 3rd best when compared to similar studies of different location. The best MAPE is 6.78% (trained with ANN & SCG) and the worst MAPE is 19.1% (trained with ANN/MLFF). Hence, it can be determined that a MAPE of 10.64% is fairly acceptable, there is definitely room for improvement in terms of the predictive ability and robustness of the ANN model.

Conclusion
Accurate SR prediction can substantially lower down the impact cost pertaining to the development of solar power. Due to the reliance on clear skies and inconsistency of atmospheric pressure and other meteorological parameters, SR prediction has become a rather challenging task which requires the help of artificial intelligence in order to correctly forecast the SR. In the present study, four ANN models have been developed to predict the solar radiation in Kuala Terrenganu, Malaysia. Each model is trained with three different BP algorithm i.e., Levenberg-Marquardt, Scaled Conjugate Gradient and Bayesian Regularization. The four models are each distinct in terms of the meteorological data that was used to train and develop the neural network. Model I have temperature and humidity; Model II has temperature and windspeed; Model III has humidity and windspeed; Model IV has temperature, humidity and windspeed. The study is carried out in such a way that the best suited training algorithm and combination of meteorological data for a SR predicting ANN model can be determined. From the findings of present study, it is determined that despite having almost similar performance scores (MSE, RMSE, MAE, R & NSE), the BR algorithm still outperformed the LM algorithm in terms of predictive ability as seen from the MAPE scores. The MAPE scores of Model I -Model IV trained by BR are 10.92%, 12.51%, 10.75% and 10.64% respectively whereas the MAPE scores of the same models trained by LM are 12.39%, 12.89%, 11.79% and 11.2% respectively. Besides, SCG trained models have the worst MAPE scores as some are even < 14%. The superior feature of the ANN model developed with BR algorithm is that it has a robustly built structure and better data generalization, which allows it to prevent overfitting issues. Hence, it is concluded that BR should be used in an ANN model for SR prediction.
Besides, the results also showed a rather high correlation between the measured SR and predicted SR when temperature and relative humidity are used as the meteorological input to train the model as compared to other combination of meteorological data which includes wind speed. This distinct can be seen from the MSE, www.nature.com/scientificreports/ RMSE, R and MAPE values between Model I, II and III. From this, it is concluded that temperature and relative humidity are closely related to SR whereas wind speed has little influence over it. A study by Yazdani, et al. 35 also displayed similar results as their study results show that SR is directly proportional to atmospheric temperature while inversely proportional to the relative humidity. It is also found that wind speed does not affect solar radiation as much as the other two meteorological parameters. Nevertheless, when Model IV is developed with all three meteorological parameters as the input, it soon become the model with the best performance and highest accuracy among the four models. This also prove that the addition of wind speed parameter does enhance the learning ability of the ANN model, thus reducing the error (as seen from the MAPE score of Model IV being the lowest: 10.64%) in the predicted SR of Model IV. Finally, the results obtained does indeed prove that artificial neural network model can be used as a reliable forecasting system for solar radiation in certain location in Malaysia which shares the same climatic condition as Kuala Terrenganu. However, as ANNs are very sensitive to the initial parameters such as the input and output datasets, the performance and results might vary accordingly. For future research works, it is recommended to train the ANN models with meteorological data collected from several different location. This would help in increasing the learning ability of the model as the training datasets will be more diversified, hence providing a better predictive ability for the model.