“…Existing major country networks also influenced the network design, such as the United States Department of Energy's Atmospheric Radiation Measurement (ARM) Program (Stokes and Schwartz, 1994) and the Australia Bureau of Meteorology solar monitoring program (Forgan, 1996). More recent monitoring networks provide similar country-level examples, such as those deployed in India, South Africa, and United Arab Emirates (UAE) (Blanksby et al, 2013;Kumar et al, 2014). Integration of such ground-based data with satellite-based models can provide a comprehensive and accurate characterization (on the order of ±4% for annual GHI and ±7% to 8% for annual DNI, globally) of solar resources (Eissa et al, 2012;Hoyer-Klick et al, 2011;Alobaidi et al, 2014;Cebecauer and Š ú ri, 2012).…”
Solar radiation resource data are the foundation of information for programs of large-scale deployment of solar energy technologies. While the solar resource in Saudi Arabia and the Arabian Peninsula was believed to be significant based on limited past data, understanding the spatial and temporal variability requires significantly more data and analysis in order to optimize planning and siting solar energy power plants. This paper summarizes the analysis of the first year of broadband solar resource measurements from a new monitoring network in Saudi Arabia developed by the King Abdullah City for Atomic and Renewable Energy (K.A.CARE). The analysis used twelve months (October 2013-September 2014) of data from 30 stations distributed across the country based on one-minute measurements of Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (DHI), Direct Normal Irradiance (DNI), and related meteorological parameters. Network design, implementation, and data quality assurance are described to document the network extent and quality. For the 30 stations, the annual average daily GHI ranged from about 5700 Wh/m 2 to 6700 Wh/m 2 with consistently higher values inland and lower values along the coasts. This indicates that photovoltaic technologies would perform well at any location although extreme high temperatures (over 30°C annual average in some locations) may degrade the performance of some types of photovoltaic technologies. Annual average daily DNI was much more variable across the stations, ranging from about 4400 Wh/m 2 to over 7300 Wh/m 2 with the highest values and clearest skies in the northwest part of the country. While most regions have sufficient solar resources for concentrating solar technologies, the western inland sites with average daily totals of over 6474 Wh/m 2 (average yearly totals of 2400 kW h/m 2 /year) are superior to the eastern sites with average daily totals closer to 5510 Wh/m 2 (average yearly totals of 2000 kW h/m 2 /year). This first year of data represents the beginning of a deeper understanding of solar resource characteristics in Saudi Arabia and the Middle East. Although continued measurements are needed to understand the interannual resource variability, the current study should have significant applications for preliminary technology selection, power plant modeling, and resource forecasting.
“…Existing major country networks also influenced the network design, such as the United States Department of Energy's Atmospheric Radiation Measurement (ARM) Program (Stokes and Schwartz, 1994) and the Australia Bureau of Meteorology solar monitoring program (Forgan, 1996). More recent monitoring networks provide similar country-level examples, such as those deployed in India, South Africa, and United Arab Emirates (UAE) (Blanksby et al, 2013;Kumar et al, 2014). Integration of such ground-based data with satellite-based models can provide a comprehensive and accurate characterization (on the order of ±4% for annual GHI and ±7% to 8% for annual DNI, globally) of solar resources (Eissa et al, 2012;Hoyer-Klick et al, 2011;Alobaidi et al, 2014;Cebecauer and Š ú ri, 2012).…”
Solar radiation resource data are the foundation of information for programs of large-scale deployment of solar energy technologies. While the solar resource in Saudi Arabia and the Arabian Peninsula was believed to be significant based on limited past data, understanding the spatial and temporal variability requires significantly more data and analysis in order to optimize planning and siting solar energy power plants. This paper summarizes the analysis of the first year of broadband solar resource measurements from a new monitoring network in Saudi Arabia developed by the King Abdullah City for Atomic and Renewable Energy (K.A.CARE). The analysis used twelve months (October 2013-September 2014) of data from 30 stations distributed across the country based on one-minute measurements of Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (DHI), Direct Normal Irradiance (DNI), and related meteorological parameters. Network design, implementation, and data quality assurance are described to document the network extent and quality. For the 30 stations, the annual average daily GHI ranged from about 5700 Wh/m 2 to 6700 Wh/m 2 with consistently higher values inland and lower values along the coasts. This indicates that photovoltaic technologies would perform well at any location although extreme high temperatures (over 30°C annual average in some locations) may degrade the performance of some types of photovoltaic technologies. Annual average daily DNI was much more variable across the stations, ranging from about 4400 Wh/m 2 to over 7300 Wh/m 2 with the highest values and clearest skies in the northwest part of the country. While most regions have sufficient solar resources for concentrating solar technologies, the western inland sites with average daily totals of over 6474 Wh/m 2 (average yearly totals of 2400 kW h/m 2 /year) are superior to the eastern sites with average daily totals closer to 5510 Wh/m 2 (average yearly totals of 2000 kW h/m 2 /year). This first year of data represents the beginning of a deeper understanding of solar resource characteristics in Saudi Arabia and the Middle East. Although continued measurements are needed to understand the interannual resource variability, the current study should have significant applications for preliminary technology selection, power plant modeling, and resource forecasting.
“…The setup was installed by the National Institute of Wind Energy (NIWE), an autonomous R&D institution that is under the Ministry of New and Renewable Energy (MNRE), Government of India [17]. The standards of the setup can be seen from [18]. Fig.…”
Solar radiation is a critical requirement for all solar power plants. As it is a time-varying quantity, the power output of any solar power plant is also time variant in nature. Hence, for the prediction of probable electricity generation for a few days in advance, for any solar power plant, forecasting solar radiation a few days into the future is vital. Hourly forecasting for a few days in advance may help a utility or ISO in the bidding process. In this study, 1-day-ahead to 6-day-ahead hourly solar radiation forecasting was been performed using the MARS, CART, M5 and random forest models. The data required for the forecasting were collected from a solar radiation resource setup, commissioned by an autonomous body of the Government of India in Gorakhpur, India. From the results, it was determined that, for the present study, the random forest model provided the best results, whereas the CART model presented the worst results among all four models considered.
“…India is a good place for illustrating site adaptation due to its challenging climatic conditions associated to the monsoon and to the atmospheric aerosol loads. India is showing a high growth in large-scale deployment of solar power plants and in consequence is setting up over 50 new meteorological stations for measuring the direct, global and diffuse solar radiation [22,23]. The solar resources expected in India are generally high and particularly Rajasthan and Gujarat regions are believed to receive the maximum direct normal irradiance in the country [24].…”
a b s t r a c tA simple method for correcting satellite-derived direct normal irradiance, with important deviations to the experimental data, is presented in this work and illustrated for the particular case of Rajasthan (India). Northwest India is expected to have a high level of solar radiation and it is an interesting area for solar concentrating power systems. However uncertainty in direct normal irradiance estimations from satellite may affect negatively to the bankability of solar plants. Direct normal irradiance have been estimated for a site in Rajasthan from satellite information for the period 2003 to 2011, and ground measurements during 2011 have been used to analyze the uncertainties and to develop a simple correction method. The original satellite estimations showed important deviations from the experimental values and high bias. A systematic underestimation of direct irradiance has been observed during the dryer seasons that could be attributed to an overestimation of the aerosol optical depth input to the model. These observations have allowed the design of a correction methodology. Unbiased new estimations of direct normal irradiance have been generated with this methodology with important reduction in the deviations and with an agreement in the distribution functions compared to the distribution function of the ground data.
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