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Solar energy forecasting and resource assessment /

Addresses new barriers to solar energy implementation that have made the field of solar forecasting and resource assessment pivotally important. Topics covered include intermittency, reliability, accuracy of long-term resource projections, and variable short-term power generation.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Kleissl, Jan
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Oxford, UK ; Waltham, MA : Academic Press, 2013.
Edición:1st ed.
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 1. Terms and definitions / Tom Stoffel
  • 2. Semi-empirical satellite models / Richard Perez, Tomas Cebecauer and Marcel Suri
  • 3. Physically based satellite models / Steven D. Miller, Andrew K. Heidinger and Manajit Sengupta
  • 4. Evaluation of resource risk in solar-project financing / Andrew McMahan, Cathy Grover and Frank Vignola
  • 5. Bankable solar-radiation datasets / Frank Vignola, Andrew McMahan and Cathy Grover
  • 6. Solar resource variability / Richard Perez and Thomas E. Hoff
  • 7. Quantifying and simulating solar-plant variability using irradiance data / M. Lave, J. Kleissl and J. Stein
  • 8. Overview of solar-forecasting methods and a metric for accuracy evaluation / Carlos F.M. Coimbra and Jan Kleissl, Richardo Marquez
  • 9. Sky-imaging systems for short-term forecasting / Bryan Urquhart, Mohamed Ghonima, Dung Nguyen, Ben Kurtz, Chi Wai Chow and Jan Kleissl
  • 10. Solar anywhere forecasting / Richard Perez and Tom E. Hoff
  • 11. Satellite-based irradiance and power forecasting for the German energy market / Jan Kuhnert, Elke Lorenz and Detlev Heinemann
  • 12. Forecasting solar irradiance with numerical weather prediction models / Vincent E. Larson
  • 13. Data assimilation in numerical weather prediction and sample applications / Andrew S. Jones and Steven J. Fletcher
  • 14. Case studies of solar forecasting with the weather research and forecasting model at GL-Garrad Hassan / Patrick Mathiesen, Jan Kleissl and Craig Collier
  • 15. Stochastic-learning methods / Carlos F.M. Coimbra and Hugo T.C. Pedro.