Page 140 - Advances in Renewable Energies and Power Technologies
P. 140
References 113
[67] M.S. Ghonima, B. Urquhart, C.W. Chow, J.E. Shields, A. Cazorla, J. Kleissl, A method
for cloud detection and opacity classification based on ground based sky imagery,
Atmos. Meas. Tech. Discuss. 5 (4) (2012) 4535e4569.
[68] B. Thurairajah, J.A. Shaw, Cloud statistics measured with the infrared cloud imager
(ICI), Geosci. Rem. Sens. 43 (2005) 2000e2007.
[69] R. Marquez, V.G. Gueorguiev, C.F.M. Coimbra, Forecasting of global horizontal irradi-
ance using sky cover indices, J. Solar Energy Eng. 135 (2013).
[70] D. Yang, P. Jirutitijaroen, W.M. Walsh, Hourly solar irradiance time series forecasting
using cloud cover index, Solar Energy 86 (2012) 3531e3543.
[71] H. Yang, B. Kurtz, D. Nguyen, B. Urquhart, C. Wai Chow, M. Ghonima, J. Kleissl, Solar
irradiance forecasting using a ground-based sky imager developed at UC San Diego, So-
lar Energy 103 (2014) 502e524.
[72] R. Marquez, C.F.M. Coimbra, Intra-hour DNI forecasting based on cloud tracking im-
age analysis, Solar Energy 91 (2013) 327e336.
[73] A. Hammer, D. Heinemann, C. Hoyer, R. Kuhlemann, E. Lorenz, R. Mu ¨ller,
H.G. Beyer, Solar energy assessment using remote sensing technologies, Rem. Sens.
Environ. 86 (2003) 423e432.
[74] J. Betcke, R. Kuhlemann, A. Hammer, A. Drews, E. Lorenz, M. Girodo, W. Krebs,
Energy-Specific Solar Radiation Data from Meteosat Second Generation (MSG): The
Heliosat-3 Project, Energy and Semiconductor Research Laboratory, 2006. Technical
Report of the Heliosat-3 Project (D17).
[75] K.F. Dagestad, Mean bias deviation of the Heliostat algorithm for varying cloud prop-
erties and sun-ground-satellite geometry, Theor. Appl. Technol. 79 (2004) 215e224.
[76] K.F. Dagestad, Estimating Global Radiation at Ground Level from Satellite Images
(Ph.D. dissertation), Dept. of Meteorology, University of Bergen, Norway, 2005.
[77] K.F. Dagestad, J.A. Olseth, An Alternative Algorithm for Calculating the Cloud Index,
2005. Heliosat-3 Projekt-Bericht.
[78] R.W. Mueller, K.F. Dagestad, P. Ineichen, M. Schroedter-Homscheidt, S. Cros,
D. Dumortier, R. Kuhlemann, J.A. Olseth, G. Piernavieja, C. Reise, L. Wald,
D. Heinemann, Rethinking satellite-based solar irradiance modelling: The SOLIS
clear-sky module, Rem. Sens. Environ. 91 (2004) 160e174.
[79] K.M. Bedka, J.R. Mecikalski, Application of satellite-derived atmospheric motion vec-
tors for estimating mesoscale flows, J. Appl. Meteor. 44 (2005) 1761e1772.
[80] J.L. Bosch, Y. Zheng, J. Kleissl, Deriving cloud velocity from an array of solar radiation
measurements, Solar Energy 87 (2013) 196e203.
[81] C.S. Velden, T.L. Olander, S. Wanzong, The impact of multi-spectral GOES-8 wind in-
formation on Atlantic tropical cyclone track forecasts in 1995. Part I: Dataset method-
ology, description, and case analysis, Mon. Wea. Rev. 126 (1998) 1202e1218.
[82] E. Lorenz, J. Ku ¨hnert, A. Hammer, D. Heinemann, Satellite Based Short Term Fore-
casting, Summer School “From Renewable Energy Production to End Users” 2.5.
2013, Montegut, France, 2013.
[83] R. Perez, S. Kivalov, J. Schlemmer, K. Hemker Jr., D. Renne ´, T.E. Hoff, Validation of
short and medium term operational solar radiation forecasts in the US, Solar Energy 84
(2010) 2161e2172.
[84] R. Perez, S. Kivalov, A. Zelenka, J. Schlemmer, K. Hemker Jr., Improving the perfor-
mance of satellite-to-irradiance models using the satellite’s infrared sensors, in: Proc.,
ASES Annual Conference, 2010.