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112    CHAPTER 3 Forecasting of Intermittent Solar Energy Resource




                         [50] Western Governors’ Association (WGA), Meeting Renewable Energy Targets in the
                             West at Least Cost: The Integration Challenge, Western Governors’ Association, Den-
                             ver, CO, 2012.
                         [51] G.E. Energy, Western Wind and Solar Integration Study (WWSIS), NREL/SR-
                             550e47434, National Renewable Energy Laboratory, Golden, CO, 2010.
                         [52] K. Orwig, B.M. Hodge, G. Brinkman, E. Ela, M. Milligan, V. Banunarayanan, S. Nasir,
                             J. Freedman, Economic evaluation of short-term wind power forecasts in ERCOT: pre-
                             liminary results, in: 11th Annual International Workshop on Large-Scale Integration of
                             Wind Power into Power Systems as well as on Transmission Networks for Offshore
                             Wind Power Plants Conference, Lisbon, Portugal, November 13e15, 2012, 2012.
                             NREL/CP-5500-56257.
                         [53] E.V. Mc Garrigle, P.G. Leahy, Quantifying the value of improved wind energy forecasts
                             in a pool-based electricity market, Renew. Energy 80 (2015) 517e524.
                         [54] D. Lew, M. Milligan, G. Jordan, D. Piwko, The Value of Wind Power Forecasting Pre-
                             print, Prepared for the American Meteorological Society Annual Meeting, January 26,
                             2011. NREL/CP-5500e50814, National Renewable Energy Laboratory, Golden, CO,
                             2011.
                         [55] R. Piwko, The value of wind power forecasting, in: Utility Wind Integration Group
                             Workshop on Wind Forecasting Applications for Utility Planning and Operations,
                             February 18e19, 2009, 2009.
                         [56] C. Voyant, G. Notton, S. Kalogirou, M.L. Nivet, C. Paoli, F. Motte, A. Fouilloy, Ma-
                             chine learning methods for solar radiation forecasting: a review, Renew. Energy 105
                             (2017) 569e582.
                         [57] S. Sperati, S. Alessandrini, P. Pinson, G. Kariniotakis, The “Weather Intelligence for
                             Renewable Energies” benchmarking exercise on short-term forecasting of wind and so-
                             lar power generation, Energies 8 (2015) 9594e9619.
                         [58] COST, Weather Intelligence for Renewable Energies (WIRE), Current State Report No.
                             ES1002, 2012.
                         [59] S. Pelland, J. Remund, J. Kleissl, T. Oozeki, K. De Brabandere, Photovoltaic and Solar
                             Forecasting: State of the Art. IEA PVPS Task 14, Subtask 3.1, 2013. Report IEA-PVPS
                             T14-01.
                         [60] J. Najac, Wind and photovoltaic production forecasting in EDF, in: In’Tech Seminar,
                             INRIA, Grenoble, France, 2012 (in French).
                         [61] Reuniwatt (2017). Website reuniwatt.com (consulted in April 2017).
                         [62] C.W. Chow, B. Urquhart, J. Kleissl, M. Lave, A. Dominguez, J. Shields, B. Washom,
                             Intra-hour forecasting with a total sky imager at the UC San Diego solar energy
                             testbed, Solar Energy 85 (11) (2011) 2881e2893.
                         [63] T. Tooman, Whole Sky Imager Retrieval Guide, U.S. Dept. Energy, Washington, DC,
                             1997.
                         [64] C.N. Long, D.W. Slater, T. Tooman, Total Sky Imager Model 880 Status and Testing
                             Results, DOE/SC-ARM/TR-006 Report, U.S. Dept. Energy, 2001.
                         [65] B. Thurairajah, Thermal Infrared Imaging of the Atmosphere: The Infrared Cloud
                             Imager (Ph.D. dissertation), Montana State University e Bozeman, College of Engi-
                             neering, 2004.
                         [66] S. Lespinats, G. Stoops, T. Pistarino, X. Le Pivert, Forecasting the solar production from
                             a plurality of sources, in: 29th European Photovoltaic Solar Energy Conference and
                             Exhibition, 2014.
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