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Tytuł artykułu

Variation in Direct Solar Irradiation with Relative Humidity and Atmospheric Temperature

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A class first Pyranometer was used to measure the direct solar irradiation and the obtained results were analyzed. Separate sensors were used to measure the relative humidity and the atmospheric temperature (RH/AT sensors). The measurements were conducted for the whole year of 2013. The author analyzed the collected solar data during one day, month, and year. The maximum, minimum, average, and accumulative solar irradiation (SR) were calculated and analyzed with connection to relative humidity and atmospheric temperature. The author found that April was characterized by the average maximum solar irradiation (SR= 1112±1W/m2), May had the maximum SR accumulation (13276552± 1W/m2). The maximum average percentage relative humidity found in July (% RH = 97± 3%), while the minimum average % RH is in December (% RH = 25± 3%). In May, the maximum average atmospheric temperature was 36.0 ± 0.6°C, while the average minimum was in January (AT= 19.1 ± 0.6°C). The total accumulation of solar flux during the year 2013 is 222 ± 1MW/m2.
Rocznik
Strony
38--43
Opis fizyczny
Bibliogr. 13 poz., rys., tab.
Twórcy
autor
  • Chemical Engineering Department, College of Engineering, Dhofar University, P.O. Box 2509, 211 Salalah, Oman
  • Chemical Engineering Department, College of Engineering, Dhofar University, P.O. Box 2509, 211 Salalah, Oman
Bibliografia
  • 1. AlYahya, S. and M.A. Irfan. 2016. Analysis from the new solar radiation Atlas for Saudi Arabia. Solar Energy, 130: 116–127.
  • 2. Ertekin, C. and Yaldız. O., 1999. Estimation of monthly average daily global radiation on horizontal surface for Antalya (Turkey). Renewable Energy, 17(1): p. 95–102.
  • 3. Gairaa, K. and S. Benkaciali,. 2011. Analysis of solar radiation measurements at Ghardaïa area, south Algeria. Energy Procedia, 6: 122–129.
  • 4. Jimenez. V.A., Barrionuevo. A., Will. A., Rodríguez. S. 2016. Neural Network for Estimating Daily Global Solar Radiation Using Temperature, Humidity and Pressure as Unique Climatic Input Variables. Smart Grid and Renewable Energy, 7(03): 94–103.
  • 5. Lee, K., Yoo. H., and Levermore., G.J., 2013. Quality control and estimation hourly solar irradiation on inclined surfaces in South Korea. Renewable energy, 57: 190–199.
  • 6. López, G., M.A. Rubio, and F.J. Batlles, 2000. Estimation of hourly direct normal from measured global solar irradiance in Spain. Renewable Energy, 21(2): 175–186.
  • 7. Loutfi, H., Bernatchou. A., Raoui. Y., Tadili. R, 2017. Learning processes to predict the hourly global, direct, and diffuse solar irradiance from daily global radiation with artificial neural networks. International Journal of Photoenergy, doi.org/10.1155/2017/4025283
  • 8. Matsumoto. Y., Valdés. M., Urbano. J. A., Kobayashi. T., López. G., Peña. R. 2014. Global solar irradiation in north Mexico city and some comparisons with the south. Energy Procedia, 57: 1179–1188.
  • 9. Paulescu. M., Paulescu. E., Gravila. P., Badescu. V. 2012. Weather modeling and forecasting of PV systems operation. Springer, Berlin Heidelberg New York.
  • 10. Pérez. B. A., Bilbao. J., De Miguel. A., Roman. R. 2014. Analysis of solar direct irradiance in Spain. Energy procedia, 57: 1070–1076.
  • 11. Scharmer. K., Greif. J., 2000. The European Solar Radiation Atlas. Les Presses de l’École des Mines: Paris.
  • 12. Sabziparver A., Shetaee H. 2007. Estimation of global solar radiation in arid and semi-arid climates of East and West Iran. Energy, 32: 649–655.
  • 13. Wazwaza, A., & Salmi. J., 2010. Solar Net Collective Flux and Conversion Efficiency of the NickelPigmented Aluminium Oxide Selective Absorber Prepared by Alternate and Reverse Periodic Technique in Different Prototype Volumes. JJMIE, 4(1).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3daf5ba4-48d1-455a-be42-93a9c4920606
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