UAE Solar Resource

The UAE Solar Atlas April 2013 by the Research Center for Renewable Energy Mapping and Assessment (ReCREMA)

The UAE Solar Atlas

Disclaimer This report was prepared by the Masdar Institute of Science & Technology (MI) and sponsored by the Ministry of Foreign Affairs for the United Arab Emirates (MoFA), the Dubai Supreme Council of Energy (DSCE) , and the Environment Agency of Abu Dhabi (EAD). Neither MI, the UAE Government, MoFA, DSCE, EAD nor any other agency of the United Arab Emirates (UAE), Dubai or Abu Dhabi governments, nor any of their respective employees, agents and/or assigns, make any warranty, express or implied, and shall not be liable or responsible for, the accuracy, completeness, or usefulness of any information, apparatus, product, and/or process disclosed, nor represent that its use would not infringe the rights of any third party. The views and opinions of authors expressed herein do not necessarily state or reflect those of the UAE, Dubai and/or Abu Dhabi governments or any agency thereof.

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List of abbreviations CSP: concentrating solar power DHI: diffuse horizontal irradiance (W/m2) DNI: direct normal irradiance (W/m2) GHI: global horizontal irradiance (W/m2) GTI: global tilt irradiance (W/m2) HRV: high-resolution visible channel IRENA: International Renewable Energy Agency MSG: Meteosat Second Generation satellite ReCREMA: Research Center for Renewable Energy Mapping and Assessment rMBE: relative mean bias error (%) rRMSE: relative root mean square error (%) RSP: Rotating Shadowband Pyranometer SEVIRI: Spinning Enhanced Visible and Infrared Imager SPV: solar photovoltaic UAE: United Arab Emirates

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1. Overview The Middle Eastern countries are among the largest oil and gas producing nations, holding approximately 54.4% oil and 40.5% gas of the world’s total share of proven reserves (BP, 2011). They are also major contributors of per capita ecological footprint with UAE topping the list of 150 countries as per WWF’s 2010 Living Planet report (WWF, 2010). Amidst rising global environmental concerns and increased awareness of dwindling fossil fuel reserves and vulnerable oil prices, a switch towards more energy efficient ways and integration of alternative resources in the current energy matrix of the Middle East is deemed inevitable. Historically reliant on fossil fuels for their economy, the GCC nations- Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and United Arab Emirates (UAE) - are now determined to foster a meaningful renewable energy share in their total energy production. Among the countries with discrete renewable energy targets are the two Emirates of Abu Dhabi and Dubai with an aim of 7% and 5% by 2020 and 2030, respectively. Several sizeable solar energy capacity additions are currently under way to meet the goal. The Arabian Peninsula lies in virtually rainless sunny belt with a typical daily average solar radiation exceeding 6 kWh/m2 (Alnaser and Alnaser, 2011) and 80-90% clear skies throughout the year. Among all forms of alternate energy, solar power therefore is a preferred choice, for its seemingly limitless potential in the GCC region and relatively well-developed technology and profitability. There is a host of existing and upcoming solar energy projects in the region with diverse applications, primarily in the form of technology clusters, utility-scale solar power plants, solar desalination projects and solar panel manufacturing industry. Abu Dhabi boasts of a 10 MW solar PV facility supplying power to Masdar in addition to other pilot projects spread across the UAE. A 100 MW concentrating solar power (CSP) plant, Shams-1, has been inaugurated in March 2013. That is to be followed by Shams-2 and Shams-3 with further capacity addition of 150 MW. Moreover, the UAE PV rooftop program aims to reach 500 MW installed capacity within 20 years. In Dubai, construction of a solar park was announced in mid-2012 with an aim to produce 10 MW by 2013 using solar photovoltaic (SPV) cell technology and 1000 MW by 2030 on the completion of all its phases (SCE, 2012). Availability of solar resource calls for reliable assessment techniques in order to obtain realistic performance estimates of a given solar application at a given location. Solar radiation data, magnitude and variability inclusive, is a key input in studying the

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economic feasibility, selecting the appropriate technology and simulating and optimizing the solar system design. Eight ground stations measuring solar irradiance have been set-up across the Abu Dhabi region covering different topographies and are operational since 2007. Although ground based solar radiation measurements are the best sources to comprehensively characterize the solar climate, such stations are fairly limited both in time (recently operational) and space (fewer sites) in the Gulf region. In the absence of long-term data at the site of interest, developers within solar technology industry, rely on solar radiation models and satellite-derived data for prompt resource assessment needs. While purely empirical models have been widely researched and validated in the Arabian Peninsula’s solar modeling history, they are known to be intrinsically sitespecific and by nature, do not address the atmospheric interactions affecting the incipient radiation. Development of more robust models requires an in-depth understanding of site’s climate, identification of key irradiance-attenuating parameters and their appropriate characterization to determine solar irradiance. Most of the existing models applied in the region typically overestimate solar irradiance, particularly direct normal irradiance (DNI) - a key input for CSP technology. The bias is primarily due to the models’ inability to adequately account for the attenuation and scattering of solar irradiance by predominant airborne dust. Modeling inaccuracies, however, cannot be overlooked as they are translated into the design of a system running heavy risk of economic as well as technical misadventures. The UAE solar atlas utilizes satellite imagery through a robust ANN model to map the solar potential across the country (details are provided in Section 2). One of the aims of the solar atlas project is to bridge the gap between restricted number of existing ground sites and regional demand for rapid solar technology deployment by providing reliable and readily available solar irradiance data. Different solar applications require different components of solar irradiance data. For example, concentrating collectors require accurate DNI estimations, while flat plate collectors require global tilt irradiance (GTI) which is derived from the knowledge of DNI, diffuse horizontal irradiance (DHI), global horizontal irradiance (GHI) and ground albedo as inputs (Gueymard, 2009; Liu and Jordan, 1963). DHI and DNI are also useful for daylight applications and cooling load calculations in energy efficient buildings (Hammer et al., 2003). Therefore, it is important to estimate all the three irradiance components i.e. GHI, DNI and DHI to make the database suitable for a wide range of solar applications.

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2. Satellite-based Irradiance Modeling/ Solar Mapping Solar maps, derived from satellite images, have the ability to estimate solar resource over areas where no ground measurements are available. Satellite based assessment is known to be more reliable than simple interpolation of ground measurements (Zelenka et al., 1999). Yearly solar maps provide irradiation data showing the solar potential in different areas for siting and performance pre-assessment purposes. They could also be used to show the temporal variability of solar radiation. Additionally, if solar maps are developed from satellite measurements, historical irradiance estimations could also be generated. Several models were developed to estimate solar radiation at the surface of the earth using geostationary satellite images. Both empirical and physical based approaches were used in these models and in some cases hybrid-based approaches were proposed by integrating both physical and empirical characteristics (Perez et al., 2002). Physical model of Gautier et al. (1980) was used to estimate the GHI in the North America using GOES images. This model was adapted for Meteosat images by Cogliani et al. (2007) to produce the physical model SOLARMET capable of measuring both the GHI and DNI over Europe, Africa and the Middle East. The Heliosat-2 model, a hybrid one, was developed to estimate the GHI using Meteosat images (Hammer et al., 2003; Rigollier et al., 2000; Rigollier et al., 2004). The original Heliosat model proposed by Cano et al., (1986) was also adapted for GOES images by Perez et al. (2002) to estimate the GHI, DNI and DHI over the Americas. Another physical-based model was developed by Schillings et al. (2004a) to estimate the DNI from Meteosat images. These models have been widely used in several locations for solar radiation estimations. Moradi et al. (2009) have applied Heliosat-2 to estimate the daily global horizontal irradiation in Iran. A relative root mean square error (rRMSE) of 11.7% and a relative mean bias error (rMBE) of +1.9% were obtained. Vignola et al. (2007) tested the model developed by Perez et al. (2002) using an independent dataset of 1-year hourly ground measurements from Kimberly, Idaho, USA. The GHI and DNI are estimated from the model, while the DHI is calculated from those estimates. Their results show rRMSE values of 21.5%, 40.9% and 54.2% for the GHI, DNI and DHI, respectively, and rMBE values of -4.9%, +2% and +15.4% in the aforementioned order. Schillings et al. (2004b) tested the model of Schilling et al. (2004a) over eight stations in Saudi Arabia for hourly satellite-derived DNI values and obtained an rRMSE of 36.1% and an rMBE of +4.3% for all sky conditions.

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2.1. Heliosat-2 corrected Performance of the Heliosat-2 model is location dependent (Rigollier et al., 2004), but no published studies were found on its application in the Arabian Peninsula. Therefore, as a starting point over the UAE, the Heliosat-2 model was applied for GHI retrievals. Ground-based measurements collected from mid-2007 to mid-2010 were used to determine physical parameters of the atmosphere. Also, the high-resolution visible (HRV) channel of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) was employed to derive cloud index n. Results have shown a constant underestimation in the estimated GHI over four stations across the UAE, shown in Fig. 1.

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b) Estimated versus Measured GHI , Station 5 y = 1.01x − 110 # of points: 7493 RMSE = 122W/m 2 rRMSE = 18.5% MBE = −104W/m 2 rMBE = −15.8%

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c) Estimated versus Measured GHI , Station 6 y = 0.999x − 106 # of points: 2720 RMSE = 125W/m 2 rRMSE = 18.3% MBE = −107W/m 2 rMBE = −15.6%

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a) Estimated versus Measured GHI , Station 4 y = 1.01x − 98.8 # of points: 2780 RMSE = 109W/m 2 rRMSE = 16.3% MBE = −90.3W/m 2 rMBE = −13.6%

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d) Estimated versus Measured GHI , Station 7 y = 1x − 97.8 # of points: 7155 RMSE = 116W/m 2 rRMSE = 17.8% MBE = −98W/m 2 rMBE = −15%

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Fig. 1: Estimated versus measured GHI for four inland stations in the UAE.

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GHI , Station 4

800 600 y = 0.997x + 7.54 # of points: 2780 RMSE = 63.5W/m 2 rRMSE = 9.54% MBE = 5.41W/m 2 rMBE = 0.812%

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c) Estimated (Corrected) versus Measured 1200

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1000 800 600 y = 0.969x + 13.2 # of points: 2720 RMSE = 69.2W/m rRMSE = 10.1% MBE = −7.91W/m rMBE = −1.16%

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To correct the bias, a recalibrated version of the Heliosat-2 model, adapted to the humid and dusty environment of the Arabian Peninsula, was proposed (Eissa et al., 2012a). The empirical clear sky DHI equation in Heliosat-2 was recalibrated using data collected from four stations in the UAE. The modified Heliosat-2 was successfully applied to estimate the GHI at a 30 min resolution. An rRMSE ranging between 9.5 to 10.3% and rMBE ranging between -1.2 to +0.8% were obtained over the four stations, shown in Fig. 2. b) Estimated (Corrected) versus Measured 1200

GHI , Station 5

1000 800 600 y = 0.995x − 4.4 # of points: 7493 RMSE = 67.3W/m rRMSE = 10.2% MBE = −7.86W/m rMBE = −1.19%

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1000 800 600 y = 0.983x + 10.7 # of points: 7155 RMSE = 67.8W/m 2 rRMSE = 10.3% MBE = −0.559W/m 2 rMBE = −0.0852%

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Fig. 2: Recalibrated Heliosat-2 GHI versus measured GHI for four inland stations.

A drawback of the Heliosat-2 method is the systematic compensation of errors in GHI estimation since DNI is generally overestimated, while DHI is underestimated (Eissa et al., 2012b). It is apparent that the accuracy of satellite-based models for the estimation of solar radiation is location dependent. Over the UAE, it was observed that Heliosat-2 method needed a recalibration before its application. Moreover, a study conducted by DLR over the UAE had overestimated the DNI (Stancich, 2012). That is mainly due to the unique environment of the Arabian Peninsula, with modest cloud coverage, high concentrations of airborne dust and high relative humidity particularly, in the coastal areas. Need for a solar mapping tool with regional coverage for UAE and similar arid environments was realized and the model, as discussed in Section 2.2, was thus developed.

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2.2. ANN Model A regionally-based statistical model to retrieve the DNI, DHI and GHI at the surface of the UAE is developed (Eissa et al., 2013) at the Research Center for Renewable Energy Mapping and Assessment (ReCREMA). This model is trained by means of a training set, containing reference inputs and outputs. The inputs to this model are the solar time, eccentricity correction (ε), day number, solar zenith angle (θZ), and six thermal channels of the SEVIRI instrument: T04 (3.9-µm); T05 (6.2-µm); T06 (7.3-µm); T07 (8.7-µm); T09 (10.8-µm) and T10 (12.0-µm). Choice of thermal channels is due to their sensitivity to the aerosols and water vapor. (Klüser et al., 2009; Martínez et al., 2009). For DNI and DHI estimations the cloudy and cloud-free observations are treated by separately trained models. As a preliminary step all inputs are gathered for each of the pixels in a specific scene. After applying a cloud mask (Hocking et al., 2010), the inputs are then fed into the respective trained ANN ensemble, i.e. cloudy or cloud-free. The final output is then taken as the median of all the trained networks, after removing the outliers by removing the top or bottom 10% of the networks based on the skewness of the outputs. For the DNI estimation, the total optical depth of the atmosphere (δ) is estimated first and the DNI is then computed using the Beer-Bouguer-Lambert law (Liou, 2002). DHI is directly estimated from the trained networks. Finally, the GHI is computed using the final estimated DNI and DHI values. The flowchart of the model is shown in Fig. 3. This model is able to produce DNI, DHI and GHI maps at a 3 km spatial resolution and in a near real-time manner, i.e. updated each 15 min. After processing the satellite images, they are then used to derive hourly, daily, monthly and yearly irradiation values for all three components.

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Fig. 3: Flowchart of the ANN model for DNI, DHI and GHI estimations.

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3. Data The solar radiation model, explained in section 2.2, was developed over the UAE using both ground data and SEVIRI data, and was then validated against independent ground data.

3.1. SEVIRI data The SEVIRI instrument is an optical imager on board of Meteosat Second Generation (MSG) satellite, having a temporal resolution of 15 min. It contains 12 spectral channels, 4 visible and near-infrared channels and 8 infrared channels. It is situated in a geostationary orbit at an altitude of 36,000 km and has a spatial resolution of 1 km for the HRV channel and 3 km for the other channels. The receiving station at the Research Center for Renewable Energy Mapping and Assessment at Masdar Institute has access to the SEVIRI images in a near real-time manner. All received data is archived. SEVIRI data is accessible since 2004.

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3.2. Ground data The above described solar radiation model is developed using SEVIRI data and is validated against the ground data.

3.2.1. Stations 10 min, hourly, daily, monthly and yearly measurements of GHI, DNI and DHI are available over six stations in the UAE. The ground measurements date back to mid2007 in some of the stations. Data is also available from a ground station in Sir Bani Yas Island and from Al Mirfa. However, those two stations were not included in the developed model because their proximity to the coast is unfavorable for satellite data retrieval. Also, Al Mirfa station was dismantled in 2009. The location of the stations is illustrated in the map presented in Fig. 4. The spatial distribution of the ground stations allows better comparison between modeled and measured solar radiation in different areas with different land cover conditions.

Fig. 4: The ground measurement stations across the UAE used in the development of the model

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3.2.2. Measurements Ground measurements are collected by a Rotating Shadowband Pyranometer (RSP). This instrument is a photodiode (silicon) sensor measuring GHI in intervals of seconds when the rotating shadowband is stationary. The shadowband then makes a whole rotation around the sensor every minute blocking the DNI, and the measured irradiance decreases. The smallest value of the measured irradiance during the rotation denotes the DHI, since at that moment all the DNI is blocked. DNI is then obtained from GHI and DHI for each 10 min interval. The instantaneous accuracies of the measurements at the stations are ±4.7% for GHI, ±6.5% for DHI and ±4.1% for DNI. The choice of measuring sensors was carefully made given that most of the UAE sites are remote and unattended. The RSP instruments are autonomous and robust; require minimal power, maintenance and cleaning for reliable operation compared to the thermopile sensors i.e. pyrheliometers for DNI, pyranometers for GHI and shadow ball pyranometer for DHI measurements which are highly sensitive to soiling.

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4. Validation

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# of points: 19720 RMSE = 150W/m 2 rRMSE = 26.1% MBE = −34.7W/m 2 rMBE = −6.01%

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# of points: 19720 RMSE = 61.1W/m 2 rRMSE = 25.6% MBE = 8.66W/m 2 rMBE = 3.63%

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Results of the applied method have been validated using different datasets. In Eissa et al. (2013) data from three stations in 2010 were employed to train the model and data from two stations in 2009 were used to validate the model. In this manner, the training and testing stations are totally independent, both in location and time (in this case a different year). At a 15 min temporal resolution and 3 km spatial resolution, for all sky conditions, the rRMSE values for the DNI, DHI and GHI for the testing stations were 26.1%, 25.6% and 12.4%, respectively, while the rMBE values were -6%, +3.6% and -2.9%, respectively. Fig. 5 shows the scatter plots for the two testing stations for all sky conditions. c) Estimated versus MeasuredGHI Testing Set for All Sky Conditions 1200 800

# of points: 19720 RMSE = 81.5W/m 2 rRMSE = 12.4% MBE = −19.4W/m 2 rMBE = −2.93%

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Fig. 5: Estimated versus measured DNI, DHI and GHI for two independent stations in the UAE.

In a different manner, for all stations the model was trained using data of 2009, and tested using data from 2010. For all sky conditions, the rRMSE values for the DNI, DHI and GHI for the testing stations were 18.1%, 19.4% and 8.8%, respectively, while the rMBE values were -2%, -0.2% and -2.5%, respectively. As shown, the model’s performance is consistent in terms of the accuracy for the testing dataset. Therefore, before applying the model to derive the final solar maps, it was trained using the full available dataset to train it with the maximum observations available at hand.

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5. Results The developed ANN model produces DNI, DHI and GHI maps at a 15 min temporal and a 3 km spatial resolution. Those maps are then used to derive the hourly, daily, monthly and yearly irradiation maps for all three components. This section will present the monthly variation in the irradiation values over six of the ground measurement stations, followed by the variations in the yearly maps and finally the conclusion.

5.1. Monthly Irradiation

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5.1.1. DNI In Fig. 6 the estimated direct normal irradiation is plotted with respect to the month for six stations in the year 2010. This figure shows how the values are highly variable throughout the different months of the year and that the month of July has the lowest values. The month of July has high atmospheric turbidity, hence the low values. Table 1 presents the errors involved over five stations with available ground measurements in 2010, no ground measurements are available over Al Sweihan in 2010. The results are within acceptable error (Gueymard, 2012), with the rMBE ranging from -6.9 to +2.4% and the rRMSE from 2.9 to 7.5%. Monthly Direct Normal Irradiation throughout the Year 2010

Estimated Direct Normal Irradiation (kWh/m 2)

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Station Masdar City East of Jabal Haffed Al Aradh Al Wagan Madinat Zayed Table 1

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rRMSE (%) 3.7 7.5 7 2.9 3.1

rMBE (%) +2.4 -6.9 -5.5 -1.43 -0.5

Estimation errors for monthly direct normal irradiation

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Direct Normal Irradiation 2010

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5.1.2. DHI In Fig. 7 the estimated diffuse horizontal irradiation is plotted with respect to the month for six stations in the year 2010. This figure shows a clear trend, starting from the winter months where the diffuse horizontal irradiation almost linearly increases to peak in July and then linearly decreases to the minimum value in December. Table 2 presents the errors involved over the five stations with available ground measurements in 2010. The rMBE ranges from -1.6 to +4% and the rRMSE from 2.7 to 5.7%. Monthly Diffuse Horizontal Irradiation throughout the Year 2010

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Station Masdar City East of Jabal Haffed Al Aradh Al Wagan Madinat Zayed

rRMSE (%) 3 5.7 3.4 2.7 2.8

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Table 2: Estimation errors for monthly diffuse horizontal irradiation

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Diffuse Horizontal Irradiation 2010

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5.1.3. GHI Fig. 8 shows the estimated global horizontal irradiation plotted with respect to the month for six stations in the year 2010. This figure shows that the months of January, February, November and December receive the least global horizontal irradiation throughout the year. It is also noticeable that in April and July there are drops in the global horizontal irradiation values when compared to their respective preceding and following months. The drop in April may be due to sandstorms which have a higher frequency of occurring, while the drop in July is due to the highly turbid atmosphere throughout the month. Table 3 presents the errors involved over those five stations with available ground measurements in 2010. The rMBE ranges from -3.1 to +1.3% and the rRMSE from 1.4 to 3.5%. Monthly Global Horizontal Irradiation throughout the Year 2010

Estimated Global Horizontal Irradiation (kWh/m 2)

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Masdar City East of Jabal Haffed Al Aradh Al Wagan Madinat Zayed Al Sweihan Jan.

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June July Aug. Sept. Month Fig. 8: Spatial variability of estimated diffuse horizontal irradiation for the year of 2010

Station Masdar City East of Jabal Haffed Al Aradh Al Wagan Madinat Zayed

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rMBE (%) +1.3 -2.6 -3.1 -1.8 +0.4

Table 3: Estimation errors for monthly global horizontal irradiation

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Global Horizontal Irradiation 2010

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5.2. Annual Irradiation 5.2.1. DNI For the years 2008 to 2010, the first observation is that in those three years there is high yearly variability in the direct normal irradiation. The year 2010 is the year with the highest direct normal irradiation values throughout the UAE, while the year 2009 has the lowest. In the year 2008 some values exceeded 2100 kWh/m2 in the Southernmost region of the country. Unfortunately, this region is uninhabited. Elsewhere in the country the values ranged from 1800 to 1950 kWh/m2, except for the Northeastern coastal region of the country where the values drop below 1800 kWh/m2. The higher concentrations of airborne dust particles due to the vicinity of the Northeastern region to Iran, along with the humidity in the vicinity of the coast cause the higher attenuation of the direct normal irradiation. It is also noticeable in this map that in the mid-coastal region of UAE the direct normal irradiation is higher than other coastal and inland regions, with values exceeding 2000 kWh/m2. The direct normal irradiation for the year of 2009 throughout most of the UAE ranged from 1700 to 1950 kWh/m2, with values exceeding that range at the same Southern and mid-coastal regions. For the year 2010, the values were much higher and ranged from 1900 to 2200 kWh/m2, with the regions exhibiting the highest values being the same ones previously mentioned.

5.2.2. DHI During the year of 2008, the diffuse horizontal irradiation values were generally above 850 kWh/m2. The mid-coastal region of the UAE shows lower values between 750 and 800 kWh/m2. For the year 2009, values were mainly over 900 kWh/m2, except for some scattered regions and the Southern and mid-coastal regions. In 2010, the diffuse horizontal irradiation values were lower than the previous two years. The values ranged from 825 to 875 kWh/m2, except in some small regions were this range was exceeded.

5.2.3. GHI Yearly global horizontal irradiation values ranged from 2100 to 2300 kWh/m2 for all three years, with values increasing from North to South. However, the year 2010 shows higher values throughout the UAE than 2008 and 2009.

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Direct Normal Irradiation 2008 / 2010

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Diffuse Horizontal Irradiation 2008 / 2010

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Global Horizontal Irradiation 2008 / 2010

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6. Conclusions Countries in the Arabian Peninsula due to their vast solar potential are promising candidates for solar power deployment. To support larger dissemination of solar technology in the Gulf region and attract investments for new solar energy capacity, therefore, requires an accurate knowledge of the spatial and temporal distribution of solar resources with detailed and bankable data for specific candidate sites. ReCREMA, in response to UAE’s renewable energy drive, has developed a solar mapping tool presented here-in for the entire country to meet prospecting and resource assessment needs. Solar irradiance is derived at a 15 min temporal and a 3 km spatial resolution using six SEVIRI thermal channels, and it has shown promising results when validated against independent ground measurements. This model is then applied to derive daily, monthly and yearly irradiation maps for the years 2008 to 2010 over the UAE. Annual ranges of direct, diffuse and global horizontal irradiation obtained from the model vary between 1800-2200, 750-900, 2100-2300 kWh/m2, respectively, over the course of three years for the UAE region. Monthly irradiation values indicate lowest DNI for the month of July. There is noticeably high intra-month variation in the DNI and the trends are similar for all the six stations. DNI irradiation has distinct ‘lows’ in February, April, July and November with respect to proceeding and succeeding months. Starting from the winter months DHI almost linearly increases to peak in July and then linearly decreases to the minimum value in December. GHI is characteristically lower for winter months than summers with a distinct peak appearing in May. UAE solar atlas development is an integral part of the ReCREMA’s goal to support the International Renewable Energy Agency (IRENA) in its advancement of a publiclyaccessible atlas of solar and wind resources, particularly for developing countries. As a next step to resource assessment, solar technologies for the UAE will be assessed and proposed based on resource quality, land use, and grid connectivity through collaboration with local and international partners.

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Key Contributors to the UAE Solar Atlas: Dr. Hosni Ghedira, Director Research Center for Renewable Energy Mapping and Assessment at Masdar Institute Areas of Expertise: Application of remote sensing in environmental monitoring, solar and wind potential mapping, water resources management in water-scarce regions, satellite image processing Dr. Taha Ouarda, Science Director Research Center for Renewable Energy Mapping and Assessment at Masdar Institute Areas of Expertise: Estimation of extreme hydro-meteorological events, modeling of hydro-meteorological variables, computational hydrology, environmental analysis, links between climate evolution, the environment, and public health. Dr. Imen Gherboudj Research Associate, Passive and Active Remote Sensing and Satellite Data Management Areas of Expertise: Dust, soil moisture, MSG/SEVIRI, SMOS and modeling Dr. Prashanth R Marpu Post-doctoral Researcher, Satellite Data Processing and High Performance Computing Areas of Expertise: Image analysis, statistical methods for large data analysis, remote sensing Dr. Saima Munawwar Post-doctoral Researcher, Solar Radiation Modeling and Scientific Writing Areas of Expertise: Solar resource assessment, irradiance data quality control and analysis, radiative transfer modeling Yehia Eissa Research engineer, Solar Mapping Areas of Expertise: Satellite-based solar radiation modeling, dust effects on solar radiation, solar mapping Jacinto Estima Research engineer, GIS developer Areas of Expertise: Geographic Information Systems (GIS) development and integration, process automation, OGC standards. Ibrahim Al Sharif Research Assistant, Assessment of Atmospheric Effects on Solar Irradiance Areas of Expertise: Solar radiation modeling, atmospheric effects on solar radiation Nada Al Meqbali Research Assistant, Dust Mapping and Monitoring Areas of Expertise: Satellite data processing, statistical modeling, dust estimation from satellite data

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Acknowledgements We are grateful to our funding sponsors (MoFA DECC, DSCE and EAD) and inkind contributors (NCMS and Masdar Clean Energy) whose financial support was instrumental in bringing this project to fruition.

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7. Publications Published journal articles 1. Eissa, Y., Chiesa, M., Ghedira, H., 2012. Assessment and recalibration of the Heliosat-2 method in global horizontal irradiance modeling over the desert environment of the UAE. Solar Energy. 86, 1816-1825. 2. Eissa, Y., Marpu, P., Gherboudj, I., Ghedira, H., Ouarda, T., Chiesa, M., 2013. Artificial neural network based model for retrieval of the direct normal, diffuse horizontal and global horizontal irradiances using SEVIRI images. Solar Energy. 89, 1–16. 3. Lazzarini, M., Marpu, P., Ghedira, H., 2013. Temperature-land cover interactions: The inversion of urban heat island phenomenon in desert city areas. Remote Sensing of Environment. 130, 136-152. 4. Parajuli, S., Gherboudj, I., Ghedira, H., 2013. The effect of soil moisture and wind speed on aerosol optical thickness retrieval in a desert environment using SEVIRI thermal channels. International Journal of Remote Sensing. 34(14), 5054-5071. Journal articles in press/ accepted for publication 1. Lazzarini, M., Ghedira, H., 2013. Assimilation of Earth Observation Variables and Solar Radiation Parameters in a Surface Energy Balance Model: a Case Study of a Desert City Area. Accepted in IEEE Geoscience and Remote Sensing Letters. Conference Proceedings 1. Al-Shehhi, M.R., Saffarini, R., Farhat, A., Al-Meqbali, N.K., Ghedira, H., Evaluating the effect of soil moisture, surface temperature, and humidity variations on MODIS-derived NDVI values. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Vancouver, Canada, pp. 3160-3163 (24-29 July, 2011). 2 Parajuli, S.P., Ghedira, H., Gherboudj, I., Effect of soil moisture and land cover on dust generation in desert and arid environment. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Vancouver, Canada, pp. 3070-3073 (24-29 July, 2011). 3. Eissa, Y., Ghedira, H., Seasonal and geographical variation of Linke turbidity factor and its effect on global horizontal irradiance estimation: UAE case study. Abstract# A11H-0188 presented at 2011 Fall Meeting, American Geophysical Union, AGU, San Francisco, Calif. (5-9 Dec, 2011). 4. Gherboudj, I., Parajuli, S. P., Ghedira, H., Evaluation of SEVIRI Thermal Infra-Red data for airborne dust detection in an arid regions: the UAE case study. Abstract# A53C0188 presented at 2011 Fall Meeting, AGU, San Francisco, Calif. (5-9 Dec, 2011).

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5. Lee, W.; Ghedira, H.; Ouarda, T.; Gherboudj, I. (2011) Analysis and characterization of the vertical wind profile in UAE. Abstract # GC41D-0845 presented at 2011 Fall Meeting, AGU, San Francisco, Calif. (5-9 Dec, 2011). 6. Moran, S. M., Bilair, S., Isaksen, L., de Rosnay, P., Zhan, X., Ghedira, H., Yang, Z., Mueller, R., Ines, A. M., Zebiak, S. E., Champagne, C., Brown, M., Escobar, V., Weiss, B., Pre-launch research to integrate NASA SMAP soil moisture and freeze/thaw state products in applications. Abstract# H21J-07 presented at 2011 Fall Meeting, AGU, San Francisco, Calif. (5-9 Dec, 2011). 7. Parajuli, S. P., Ghedira, H., Gherboudj, I., A neural networks based method to estimate higher resolution aerosol optical thickness using historical data sets of SEVIRI dust indices, soil moisture and wind speed. Abstract# A53C-0368 presented at 2011 Fall Meeting, AGU, San Francisco, Calif. (5-9 Dec, 2011). 8. Ghedira, H., Gherboudj, I., Parajuli, S., Evaluation of simulated SMAP data on airborne dust monitoring and mapping over the UAE. 12th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, University of Rome, Italy (5-9 Mar, 2012). 9. Eissa, Y., Ghedira, H., Chiesa, M., Marpu, P. R., Ouarda, T. B.M.J., Recalibration of Heliosat-2 method for global horizontal irradiance estimation in dusty and humid environments. World Renewable Energy Forum, Denver, CO, ID#0206 (13-17 May, 2012). 10. Eissa, Y., Marpu, P. R., Ghedira, H., Ouarda, T. B.M.J., Chiesa, M., Analyzing temporal and spatial variations of direct normal, diffuse horizontal and global horizontal irradiances estimated from an artificial neural network based model. World Renewable Energy Forum, Denver, CO, ID#0279 (13-17 May, 2012). 11. Eissa, Y., Marpu, P. R., Ghedira, H., Ouarda, T. B.M.J., Chiesa, M., An artificial neural network based approach for estimating direct normal, diffuse horizontal and global horizontal irradiances using satellite images. World Renewable Energy Forum, Denver, CO, ID#0204 (13-17 May, 2012). 12. Al-Shehhi, M., Gherboudj, I., H. Ghedira., Temporal-spatial analysis of chlorophyll concentration associated with dust and wind characteristics in the Arabian Gulf. Proceedings of the OCEANS’12, MTS/IEEE, Yeosu, South Korea 120112-013 (21-24 May, 2012). 13. Lazzarini, M., Marpu, P., H. Ghedira., Temperature-land cover interactions: the inversion of urban heat Island phenomenon in desert city areas. Taking the temperature of the Earth Workshop, Edinburgh, UK (25-27 June, 2012). 14. Parajuli, S., Gherboudj, I., and Ghedira, H., Evaluation of the effect of soil moisture and wind speed on dust emission using AERONET, SEVIRI, soil moisture and wind speed data. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, pp. 1329-1332 (22-27 July, 2012).

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15. Eissa, Y., Ghedira, H., Ouarda, T., Chiesa, M., Dust detection over bright surfaces using high-resolution visible SEVIRI images. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, pp. 3674-3677 (22-27 July, 2012). 16. Gherboudj, I., Ghedira, H., Retrieving Aerosol Optical Thickness (AOT) over the desert of the United Arab Emirates (UAE) using MSG/SEVIRI infrared measurements. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, pp. 4213-4216 (22-27 July, 2012). 17. Al Shehhi, M., Gherboudj, I., Ghedira, H., A study on the effect of dust and wind on phytoplankton activities in the Arabian Gulf. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, pp. 2571-2574 (22-27 July, 2012). 18. Lazzarini, M., Marpu, P., Ghedira, H., Land cover and land surface temperature interactions in desert areas: a case study of Abu Dhabi (UAE). IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, pp. 63256328 (22-27 July, 2012). 19. Lazzarini, M. and Ghedira, H., Temporal analysis of urban heat island and radiation balance for a desert urban area. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany (22-27 July, 2012). 20. Munawwar, S., Eissa, Y., Ghedira, H., Chiesa, M., Challenges of satellite-based solar resource assessment in dusty environment: the UAE case study. SolarPACES, Marrakech, Morocco (11-14 Sept, 2012). 21. Oumbe, A., H. Bru, Z. Hassar, P. Blanc, L. Wald, A. Fournier, D. Goffe, M. Chiesa, H. Ghedira, Selection and implementation of aerosol data for the prediction of solar resource in United Arab Emirates. SolarPACES, Marrakech, Morocco (11-14 Sept, 2012). 22. Eissa, Y., Marpu, P. R., Ghedira, H., Chiesa, M., SEVIRI-derived direct normal irradiance using Heliosat-2 global horizontal irradiance and a neural network ensemble: a comparison. EuroSun, Rijeka, Croatia (18-20 Sept, 2012). 23. Eissa, Y., Marpu, P. R., Ghedira, H., Ouarda, T. B.M.J., Chiesa, M., Near-nowcasting tool for direct normal and global horizontal irradiance estimations using SEVIRI images. EuroSun, Rijeka, Croatia (18-20 Sept, 2012). 24. Eissa, Y., Marpu, P. R., Ghedira, H., Chiesa, M., Temporal and spatial assessment of yearly solar maps derived from satellite images over the UAE and Qatar. Abstract# A11I-0164 presented at 2012 Fall Meeting, AGU, San Francisco, Calif. (3-7 Dec, 2012). 25. Ghedira, H., Eissa, Y., A comparison between Heliosat-2 and artificial neural network methods for global horizontal irradiance retrievals over desert environments. Abstract# A31F-0084 presented at 2012 Fall Meeting, AGU, San Francisco, Calif. (3-7 Dec, 2012).

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26. Marpu, P. R., Eissa, Y., Al Meqbali, N., Ghedira, H., Dust indicator maps for improving solar radiation estimation from satellite data. Abstract# A11I-0165 presented at 2012 Fall Meeting, AGU, San Francisco, Calif. (3-7 Dec, 2012). 27. Munawwar, S., Ghedira, H., Towards Building Reliable, High-Accuracy Solar Irradiance Database For Arid Climates. Abstract# A11I-0166 presented at 2012 Fall Meeting, AGU, San Francisco, Calif. (3-7 Dec, 2012). 28. Alsharif, I., Ghedira, H. (2012) Assessing the attenuating effects of water vapor and airborne dust on solar energy resources in arid environments. Abstract# A11I0168presented at 2012 Fall Meeting, AGU, San Francisco, Calif. (3-7 Dec, 2012). 29. Oumbe, A., Bru, H., Ghedira, H., Chiesa, M., Blanc, P., Wald, L. (2012) Using AERONET to complement irradiance networks on the validation of satellite-based estimations. Abstract# A11I-0172 presented at 2012 Fall Meeting, AGU, San Francisco, Calif. (3-7 Dec, 2012).

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8. Bibliography Alnaser, W.E., Alnaser, N.W., 2011. The status of renewable energy in the GCC countries. Renewable and Sustainable Energy Reviews. 15, 3074– 3098. Aminou, D., 2002. MSG’s SEVIRI instrument. ESA Bulletin, 15-17. Barutçuoğlu, Z., Alpaydın, E., 2003. A comparison of model aggregation methods for regression. In: Artificial Neural Networks and Neural Information Processing (ICANN/ ICONIP’03), 76-83. Bishop, C.M., 2006. Pattern recognition and machine learning. Springer. BP, June 2011. BP Statistical Review of World Energy. London. Breiman, L., 1996. Bagging predictors. Machine learning. 24, 123-140. Cano, D., Monget, J.M., Albuisson, M., Guillard, H., Regas, N., Wald, L., 1986. A method for the determination of the global solar radiation from meteorological satellite data. Solar Energy. 37, 31-39. Chokmani, K., Ouarda, T. B.M.J., Hamilton, S., Ghedira, H., Gingras, H., 2008. Comparison of ice-affected streamflow estimates computed using artificial neural networks and multiple regression techniques. Journal of Hydrology. 349, 383-396. Cogliani, E., Ricchiazzi, P., Maccari, A., 2007. Physical model SOLARMET for determinating total and direct solar radiation by meteosat satellite images. Solar Energy. 81, 791-798. Dietterich, T., 2000. Ensemble methods in machine learning. In: First International Workshop on Multiple Classifier Systems (MCS’00), 1-15. DLR, 2005. Concentrating solar power for the Mediterranean Region. . Dountio, E.G., Njomo, D., Fouda, E., Simo, A., 2010. On the reliability of HELIOSAT method: A comparison with experimental data. Solar Energy. 84, 1047-1058. Duffie, J.A., Beckman, W.A., 2006. Solar engineering of thermal processes, third ed. John Wiley & Sons, Inc., New Jersey. Dumortire, D, 1995. Modelling global and diffuse horizontal irradiances under cloudless skies with different turbidities. In: Final Report JOU2-CT92-0144, Daylight II, Ecole Nationale des Travaux Publics de l’Etat, Vaulx-en-Velin, France.

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Eissa, Y., Chiesa, M., Ghedira, H., 2012a. Assessment and recalibration of the Heliosat-2 method in global horizontal irradiance modeling over the desert environment of the UAE. Solar Energy. 86, 1816-1825. Eissa, Y., Marpu, P. R., Ghedira, H., Chiesa, M., 2012b. SEVIRI-derived direct normal irradiance using Heliosat-2 global horizontal irradiance and a neural network ensemble: a comparison. In EuroSun, Rijeka, Croatia, 18-20 Sept. Eissa, Y., Marpu, P. R., Gherboudj, I., Ghedira, H., Ouarda, T. B.M.J., Chiesa, M., 2013. Artificial neural network based model for retrieval of the direct normal, diffuse horizontal and global horizontal irradiances using SEVIRI images. Solar Energy. 89, 1-16. Freund, Y., Schapire, R.E., 1996. Experiments with a new boosting algorithm. In: International Conf. on Machine Learning, 148-156. Gautier, C., Diak, G., Masse, S., 1980. A simple physical model to estimate incident solar radiation at the surface from GOES satellite data. Journal of Applied Meteorology. 19, 1005-1012. Gueymard, C.A., Garrison, J.D., 1998. Critical evaluation of precipitable water and atmospheric turbidity in Canada using measured hourly solar irradiance. Solar Energy. 62, 291-307. Gueymard, C., 2009. Direct and indirect uncertainties in the prediction of tilted irradiance for solar engineering applications. Solar Energy. 83, 432-444. Gueymard, C., 2012. Solar resource assessment and forecasting: Recent achievements, bankability pressures, and current challenges. Presentation at 2012 World Renewable Energy Forum (WREF), 13-17 May 2012, Denver, CO, USA. . Hammer, A., Heinemann, D., Hoyer, C., Kuhlemann, R., Lorenz, E., Müller, R., Beyer, H.G., 2003. Solar energy assessment using remote sensing technologies. Remote Sensing of Environment. 86, 423-432. Hocking, J., Francis, P.N., Saunders, R., 2010. Cloud detection in Meteosat Second Generation imagery at the Met Office. Forecating R&D Technical Report No. 540. Huld, T., Gottschalg, R., Beyer, H.G., Topic, M., 2010. Mapping the performance of PV modules, effects of module type and data averaging. Solar Energy. 84, 324-338. Huld T., Šúri M., Dunlop E., Albuisson M, Wald L, 2005. Integration of HelioClim-1 database into PVGIS to estimate solar electricity potential in Africa. Proceedings from 20th European Photovoltaic Solar Energy Conference and Exhibition, 6-10 June 2005, Barcelona, Spain.

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Ineichen, P., Perez, R., Kmiecik, M., Renne, D., 2000. Modeling direct irradiance from GOES visible channel using generalized cloud indices. Proceeding from 10th Conference on Satellite Meteorology and Oceanography, 9-14 Jan. 2000, Long Beach, California, 133-136. Ineichen, P., Perez, R., 2002. A new airmass independent formulation for the Linke turbidity coefficient. Solar Energy. 73, 151-157. Janjai, S., Laksanaboonsong, J., Nunez, M., Thongsathitya, A., 2005. Development of a method for generating operational solar radiation maps from satellite data for a tropical environment. Solar Energy. 78, 739-751. Janjai, S., 2010. A method for estimating direct normal solar irradiation from satellite data for a tropical environment. Solar Energy. 84, 1685-1695. Kasten, F., Young, A.T., 1989. Revised optical air mass tables and approximation formula. Applied Optics. 28, 4735-4738. Klüser, L., Schepanski, K., 2009. Remote sensing of mineral dust over land with MSG infrared channels: A new Bitemporal Mineral Dust Index. Remote Sensing of Environment. 113, 1853-1867. Lillesand, T.M., Kiefer, R.W., Chipman, J.W., 2008. Remote sensing and image interpretation, sixth ed. John Wiley & Sons, Inc., New Jersey. Linke, F., 1922. Transmissions-koeffizient und trübungsfaktor. Beitr. Phys. Atmos. 10, 91-103. Liou, K.N., 2002. An introduction to atmospheric radiation, second ed. Academic Pr. Liu, B., Jordan, R., 1963. The long-term average performance of flat-plate solar energy collectors. Solar Energy. 7, 53-74. Martínez, M.A., Ruiz, J., Cuevas, E., 2009. Use of SEVIRI images and derived products in a WMO Sand and dust Storm Warning System. In: WMO/GEO Expert Meeting on an International Sand and Dust Storm Warning System, doi:10.1088/17551307/7/1/012004. Maxwell, E., George, R., Wilcox, S., 1998. A climatological solar radiation model. In: Proceedings of American Solar Energy Society Annual Conference, New Mexico, USA, 14-17 June, pp. 505-520. Mellit, A., Pavan, A.M., 2010. A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy. Solar Energy. 84, 807-821.

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Moller, M.F., 1993. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks. 6, 525-533. Moradi, I., Mueller, R., Alijani, B., Kamali, G.A., 2009. Evaluation of the Heliosat-II method using daily irradiation data for four stations in Iran. Solar Energy. 83, 150-156. Moustris, K., Paliatsos, A., Bloutsos, A., Nikolaidis, K., Koronaki, I., Kavadias, K., 2008. Use of neural networks for the creation of hourly global and diffuse solar irradiance data at representative locations in Greece. Renewable Energy. 33, 928-932. Mubiru, J., Banda, E., 2008. Estimation of monthly average daily global solar irradiation using artificial neural networks. Solar Energy. 82, 181-187. NASA, 2012. Surface meteorology and solar energy (SSE) release 6.0 Methodology. . Ouarda, T. B.M.J., Shu, C., 2009. Regional low-flow frequency analysis using single and ensemble artificial neural networks. Water Resources Research. 45, W11428. Paech, S., Mecikalski, J., Sumner, D., Pathak, C., Wu, Q., Islam, S., Sangoyomi, T., 2009. A calibrated, high-resolution GOES satellite solar insolation product for a climatology of Florida evapotranspiration. Journal of the American Water Resources Association. 45, 1328-1342. Pedrós, R., Utrillas, M., Martinez-Lozano, J., Tena, F., 1999. Values of broad band turbidity coefficients in a Mediterranean coastal site. Solar Energy. 66, 11-20. Perez, R., Ineichen, P., Moore, K., Kmiecik, M., Chain, C., George, R., Vignola, F., 2002. A new operational model for satellite-derived irradiances: description and validation. Solar Energy. 73, 307-317. Remund, J., Wald, L., Lefèvre, M., Ranchin, T., Page, J., 2003. Worldwide Linke turbidity information. Proceedings from ISES Solar World Congress, 16-19 June 2003, Goteborg, Sweden, CD-ROM published by International Solar Energy Society. Saudi Gazette, 2012. Resource mapping critical to GCC renewable energy quest. . Şenkal, O., 2010. Modeling of solar radiation using remote sensing and artificial neural network in Turkey. Energy. 35, 4795-4801. REN21, 2012. Renewables 2012 Global Status Report. Paris: REN21 Secretariat. Richter, C., Teske, S., Short, R., 2009. Global concentrating solar power - outlook 09. Greenpeace International/European Solar Thermal Electricity Association (ESTELA)/IEA SolarPACES, Report.

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Ridgeway, G., Madigan, D., Richardson, T., 1999. Boosting methodology for regression problems. In: Artificial Intelligence and Statistics, 152-161. Rigollier, C., Bauer, O., Wald, L., 2000. On the clear sky model of the ESRA—European Solar Radiation Atlas—with respect to the Heliosat method. Solar Energy. 68, 33-48. Rigollier, C., Lefèvre, M., Wald, L., 2004. The method Heliosat-2 for deriving shortwave solar radiation from satellite images. Solar Energy. 77, 159-169. Schillings, C., Mannstein, H., Meyer, R., 2004a. Operational method for deriving high resolution direct normal irradiance from satellite data. Solar Energy. 76, 475-484. Schillings, C., Meyer, R., Mannstein, H., 2004b. Validation of a method for deriving high resolution direct normal irradiance from satellite data and application for the Arabian Peninsula. Solar Energy. 76, 485-497. Stancich, R., 2012. No slip ups, contends Masdar. CSP Today. . The Supreme Council of Energy (SCE), 2012. Dubai. . Trieb, F., Schillings, C., O’Sullivan, M., Pregger, T., Hoyer-Klick, C., 2009. Global potential of concentrating solar power. Proceedings from SolarPaces Conference, 1518 Sept., Berlin, Germany. Tymvios, F., Jacovides, C., Michaelides, S., Scouteli, C., 2005. Comparative study of Ångström’s and artificial neural networks’ methodologies in estimating global solar radiation. Solar Energy. 78, 752-762. Vignola, F., Harlan, P., Perez, R., Kmiecik, M., 2007. Analysis of satellite derived beam and global solar radiation data. Solar Energy. 81, 768-772. Vonder Haar, T., Ellis, J., 1978. Determination of the solar energy microclimate of the United States using satellite data. In: Final Report, NASA Grant NAS5-22372, Colorado State University. WWF, 2010. Living planet report 2010, Gland. Zelenka, A., Perez, R., Seals, R., Renné, D., 1999. Effective accuracy of satellite-derived hourly irradiances. Theoretical and Applied Climatology. 62, 199-207.

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9. Stakeholders

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