Prediction of Global Solar Radiation in UAE

Prediction of Global Solar Radiation in UAE Using Artificial Neural Networks Ali H. Assi Department of Electrical and Electronic Engineering Lebanese International University Beirut, Lebanon ali.assi@liu.edu.lb Maitha H. Al-Shamisi Department of Electrical Engineering UAE University Al Ain, United Arab Emirates maitha.alshamisi@ieee.org Hassan A.N. Hejase Department of Electrical Engineering UAE University Al Ain, United Arab Emirates hhejase@uaeu.ac.ae Ahmad Haddad Department of Electrical and Electronic Engineering Lebanese International University Beirut, Lebanon Ahmad.haddad@liu.edu.lb Abstract This paper presents an artificial neural network (ANN) model for predication global solar radiation (GSR) for main cities in the UAE namely, Abu Dhabi, Al-Ain and Dubai. Multi-Layer Perceptron () and Radial Basis Function () techniques with comprehensive training algorithms, architectures, and different combinations of inputs are used to develop these models. The measured data include the maximum temperature ( C), mean wind speed (knot), sunshine hours, mean relative humidity (%) and mean daily global solar radiation on a horizontal surface (kwh/m 2 ). This data was provided by the National Center of Meteorology and Seismology (NCMS) of Abu Dhabi. The results show the generalization capability of ANN approach and its ability to generate accurate prediction of GSR in UAE. Keywords- Global Solar Radiation (GSR); Artificial Neural Networks; Multilayer Perceptron; Radial Basis Function; modeling; UAE. I. INTRODUCTION The UAE is located in Southwest Asia between latitudes 22.0 and 26.5 N and between longitude 51 and 56.5 E. It has an arid climate that is subject to ocean effects due to its proximity to the Arabian Gulf and the Gulf of Oman. Although generally warm and dry in the winter, coastal weather brings in humidity along with very high temperatures during the summer months [1]. Due to the high economic and demographic growth rates in the UAE, the consumption of energy kept increasing. As consequence the CO 2 emissions are increase [2]. To overcome this issue, UAE take The UAE has taken serious steps to emerge into the solar energy market by launching the outstanding MASDAR initiative in Abu Dhabi and Mohammed bin Rashid Al Maktoum Solar Park in Dubai [3, 4]. In recent years, many studies around the world have been carried out modeling solar radiation using ANN techniques. Different approaches had been proposed from different perspectives: number of inputs, learning algorithms, architectures, and network types. The published research work on GSR can be classified based on the output parameters into Hourly [5-10], Daily [11-16], Monthly [17-19], maximum solar radiation [20] and potential [21-25]; developed modeling studies to predict solar radiation in regions where no direct measurements are available. The objective of the present study is to develop UAE models that capable to predict GSR for UAE cities by using weather data provided by Abu Dhabi s National Center of Meteorology and Seismology (NCMS). The data include the maximum temperature ( C), mean wind speed (knot), sunshine (hours), mean relative humidity (%) and solar radiation (kwh/m 2 ). The meteorological data of Abu Dhabi, Al Ain and Dubai between 2002 and 2008 are used for training the ANN while data between 2009 and 2010 are used for testing and validating the predicted values. II. DATA COLLECTION AND METHODOLOGY The National Center for Meteorology and Seismology (NCMS), Abu Dhabi has kindly provided the authors with weather data for the five UAE Cities of Abu Dhabi, Al-Ain, Dubai, Sharjah, and Ras Al-Khaimah. The time periods forthe collected data are indicated in Table 1. The weather stations monitor meteorological variables such as maximum temperature (T), mean wind speed (W), sunshine hours (SH), relative humidity (RH), as well as the mean daily GSR for the 9-year daily average (2002-2010) data of Abu Dhabi, Al-Ain and Dubai. GSR data for the cities of Sharjah and Ras Al- Khaimah are not available. Table 1 shows that the temperature values for the five cities vary between 34 and 37 C o, with the highest temperature in the city of Al-Ain. Wind speed variation between Abu Dhabi, Al- Ain and Dubai is small, while Sharjah and Ras Al-Khaimah have large variation compared to other cities. In general, sunshine hours measurements of all cities are close. It is

noticeable that Al Ain city has the lowest relative humidity due to its inland location on the border with the Sultanate of Oman. On the other hand, it has the highest amount of global solar radiation. The collected information is first examined for missing and erroneous data. Missing data are replaced with the expected values of the global radiation, and suspected erroneous values are removed after careful examination. The representative UAE data is generated from the 9-year daily average data (2002-2010) of the three UAE cities (Abu Dhabi, Al-Ain and Dubai). The computed data for UAE is divided into two sets: The training dataset spanning the period 2002-2008 (7 years) which is used to develop and adjust the weights in the neural network, and a dataset for the period 2009-2010 (2 years) which is used to test the network performance. The inputs of the ANN models are selected based on previous research work [26-28]. In other words, the optimal performance model for each city is selected as a guide for choosing the inputs for the UAE model. III. RESULTS AND DISCUSSION The optimal ANN prediction models are validated using the test data set for years 2009-2010and with the measured data from the individual cities for the same time period. The performance of these models is evaluated statistically using the coefficient of determination (R 2 ), the root mean square error (RMSE), the mean bias error (MBE), and the mean absolute percentage error (MAPE). Moreover, the best performing model is utilized to predict the GSR in other UAE cities. Table 2 shows the network structure and statistical error results for the optimal ANN models. The second column represents the network structure; the first number indicates the number of neurons in the input layer, the last number represents the number of output layers, and the numbers in between represent the number of neurons in the hidden layer The R 2 values of all models are higher than 82%, RMSE values vary between 0.32 and 0.70kWh/m 2, the MBE values are less than 0.03 kwh/m 2 in absolute value, and MAPE values vary between 4.86 and 9.46 %. The (T, W, RH) model outperforms other models if MBE is taken as the comparison measure yielding the lowest MBE value of -0.00006 kwh/m 2, while the (T, W, SH) model has the worst performance as confirmed by the statistical errors. Recall that low MBE values indicate good long term prediction performance. However, the (T, W, SH, RH) model that considers all the four weather variables has the highest R 2, the lowest RMSE and MAPE values and a comparably small MBE (-0.0002 kwh/m 2 ) and thus serves as the optimal ANN model. Fig. 1 shows a comparison between the average daily GSR predicted by ANN models and the measured data. A good agreement is observed between predicted and measured data for all models. The (T, W & SH) model shows many outliers during winter season. The predicted monthly average values of GSR from the six developed models and the measured monthly average daily GSR for the test period of 2009-2010 are illustrated in Table 3. Note the good agreement between the ANN models and the measured data with the annual average daily GSR being 5.7554 kwh/m 2 and 5.7551 kwh/m 2 for the measured and (T, W, SH, RH) model, respectively. Table 4 shows that the largest deviation between the (T, W, SH, RH) model and measured data occurs for the months of May-June (overestimate), and Nov-Dec (underestimate). In this work the potential term is chosen as the developed models are utilized to estimate the solar radiation for cities where no radiation data is available. Hence, the comparison between the measured and estimation values cannot be carried out. Based on the results shown above, Model ( with T, W, RH) performs very well when weather data for that city are available. Thus, we will attempt to use this model in predicting the GSR for the cities of Sharjah and Ras Al-Khaimah. Fig. 2 shows the predicted GSR for the cities of Sharjah and Ras Al-Khaimah city. The available input data for Sharjah is for the years 2006-2007, while Ras Al-Khaimah data spans the period 2009-2010. No measured data is available to validate the predicted data. Fig. 3 shows the comparison between ANN and the regression models developed by the same authors of this paper. The optimal regression models used are the second-order polynomial (quadratic) for the UAE, the third-order polynomial (cubic) for Al-Ain, and exponential models for the cities of Abu Dhabi and Dubai [29]. The lower error values (RMSE, MBE, MABE, MBE, and MAPE) obtained for the -ANN model confirms the potential of ANN techniques for long term GSR data prediction (See Table 4). The ANN models are capable of handling random and missing data, whereas the presence of outliers negatively affects the performance of regression models. On the other hand, the regression models make use only of the sunshine-hours data and geographical location, and thus require less time and experience to be implemented. TABLE I. DAILY MEAN OF METEOROLOGICAL DATA AND MEASURING PERIODS FOR UAE CITIES Station Period Temperature (T, 0 C) Wind Speed (W, Knots) Sunshine Hours (SH) Relative Humidity (RH, % ) GSR (KWh/m 2 ) Abu Dhabi 2002-10 35.3 7.2 9.8 56.3 6 Al-Ain 2002-10 36.6 7.5 9.7 47.2 6.2 Dubai 2002-10 34.2 7.2 9.9 52.9 5.5 Sharjah 2006-07 35.4 5.7 9.7 54.6 - Ras Al-Khaimah 2009-10 36.2 4.0 9.0 59.5 -

TABLE II. NETWORK STRUCTURE AND STATISTICAL ERROR PARAMETERS OF THE DEVELOPED ANN MODELS FOR THE UAE Model Network Structure R2 RMSE MBE MAPE (T, W, SH, RH) 4 20 25 30-1 0.96 0.32204-0.00020 4.86 (T, W, SH) (T, W, RH) (T, W, SH, RH) (T, W, SH) (T, W, RH) 3 240-1 0.82 0.70866-0.02905 9.46 3 35 40-1 0.94 0.37303-0.00006 5.21 4 25-1 0.93 0.41474-0.00203 5.88 3 35-1 0.95 0.35238 0.00012 4.99 3 80-1 0.95 0.34868-0.01032 5.20 Figure 1. Performance of different ANN models for the average daily GSR (UAE)

TABLE III. PREDICTED MONTHLY AVERAGE VALUES OF GSR FROM DEVELOPED ANN MODELS ANN Models Measured (T, W, SH, RH) (T, W, SH) (T, W, RH) (T, W, SH, RH) (T, W, SH) (T, W, RH) JAN 3.8590 3.8462 FEB 4.6715 4.6990 MAR 5.5998 5.5719 APR 6.7881 6.5054 MAY 7.7246 7.5221 JUN 7.9259 7.7148 JUL 6.7342 6.7791 AUG 6.7926 6.8316 SEP 6.4614 6.3453 OCT 5.1529 5.3040 NOV 3.9845 4.3281 DEC 3.3706 3.6142 Average 5.7554 5.7551 3.9370 3.9008 3.8893 4.7551 4.6784 4.6327 5.5510 5.4487 5.4865 6.7031 6.4965 6.4591 7.8061 7.5247 7.5764 7.9217 7.7191 7.7042 6.7199 6.8083 6.7121 6.6941 6.8163 6.8031 6.6772 6.3550 6.4037 5.2822 5.2855 5.4078 4.0823 4.3931 4.3460 3.2924 3.6332 3.6559 5.7852 5.7550 5.7564 3.8673 3.8795 4.6520 4.6470 5.5218 5.5701 6.5056 6.5060 7.5198 7.4897 7.7170 7.7143 6.7695 6.7826 6.8466 6.8169 6.3990 6.3672 5.3594 5.2782 4.2857 4.3792 3.6095 3.7466 5.7544 5.7648 Figure 2. Predicted Global Solar Radiation for Sharjah and Ras Al Khaimah Figure 3. Comparison of the best prediction empirical regression (UAE and cities) and ANN (UAE) models for the monthly average daily GSR TABLE IV. MONTHLY AVERAGE DAILY GSR ERROR STATISTICS FOR THE BEST REGRESSION MODELS (UAE AND CITIES) WITH ANN MODELS FOR UAE Error ANN- ANN- UAE (Quadratic) Al-Ain (Cubic) Abu Dhabi (Exponential) Dubai (Exponential) RMSE 0.1790 0.1991 0.3549 0.4469 0.2926 0.3357 MBE -0.0002 0.0010 0.0199 0.0449-0.0277 0.0468 R 2 (%) 99.27% 99.02% 98.94% 98.99% 98.35% 96.41%

IV. CONCLUSION In this work ANN models ( and ) have been developed to predict the global solar radiation in UAE. The measured data for the three UAE cities (Abu Dhabi, Al-Ain and Dubai) for the period 2002-2008 is used to train the models, while data for 2009-2010 is used to test and validate the prediction models. In general, all models performed well with R2 above 82%. The developed models have been used to estimate the potential of global solar radiation for the cities of Sharjah and Ras Al-Khaimah. The best performing ANN models are compared with the regression models and results show that ANN techniques are more efficient in predicting the GSR. ACKNOWLEDGMENT The authors would like to thank the National Center of Meteorology and Seismology (NCMS), Abu Dhabi for providing the weather data. REFERENCES [1] H. M. Hasanean, The United Arab Emirates Initial National Communication to the United Nations Framework Convention on Climate Change. 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