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Wind and Solar Forecasting

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Part of the book series: Power Electronics and Power Systems ((PEPS))

Abstract

The non-dispatchable variability of wind and solar power production presents a substantial challenge to electric grid operators.

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Notes

  1. 1.

    http://www.wrf-model.org/index.php.

References

  1. M. Ahlstrom, et al., Knowledge is power. IEEE Power Energy Mag 11, 45–52 (2013)

    Google Scholar 

  2. J. Coffier, Fundamentals of Numerical Weather Prediction, reprint edition (Cambridge University Press, 2012), 368 pp. ISBN-10: 110700103X, ISBN-13: 978-1107001039

    Google Scholar 

  3. C. Cortes, V. Vapnik, Support-vector networks. Mach. Learn. 20(3), 273 (1995)

    MATH  Google Scholar 

  4. A. Decaria, G. van Knowe, A First Course in Atmospheric Numerical Modeling (Sundog Publishing, 2013)

    Google Scholar 

  5. L. Delle Monache, F. Eckel, D.L. Rife, B. Nagarajan, K. Searight. Probabilistic weather prediction with an analog ensemble. Mon. Wea. Rev. 141, 3498–3516 (2013). doi:10.1175/MWR-D-12-00281.1

  6. H. Drucker, C.J.C. Burges, L. Kaufman, A.J. Smola, V.N. Vapnik, Support vector regression machines, in Advances in Neural Information Processing Systems 9, NIPS 1996, (MIT Press, 1997), pp. 155–161

    Google Scholar 

  7. H.R. Glahn, D.A. Lowry, The use of model output statistics (MOS) in objective weather forecasting. J. Appl. Meteor 11, 1203–1211 (1972)

    Article  Google Scholar 

  8. T.N. Krishnamurti, L. Bounoua, An Introduction to Numerical Weather Prediction Techniques (CRC Press, 1995)

    Google Scholar 

  9. E.J. Natenberg, J. Zack, S. Young, R. Torn, J. Manobianco, C. Kamath, Application of ensemble sensitivity analysis to observational targeting for wind power forecasting, in 14th Symposium on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS), AMS Annual Meeting, Atlanta (2010)

    Google Scholar 

  10. E.J. Natenberg, J. Zack, S. Young, J. Manobianco, C. Kamath, A new approach using targeted observations to improve short term wind power forecasts, in AWEA WindPower 2010 Conference and Exhibition, Dallas, TX (2010)

    Google Scholar 

  11. A. Tuohy, J. Zack, S. Haupt, J. Sharp, M. Ahlstrom, S. Dise, E. Grimit, C. Mohrlen, M. Lange, M. Casado, J. Black, M. Marquis, C. Collier, Solar forecasting: methods, challenges, and performance. IEEE Power Energy Mag 13(6), 50–59 (2015). doi:10.1109/MPE.2015.2461351

  12. S. Vannitsem, Dynamical properties of MOS forecasts: analysis of the ECMWF operational forecasting system. Wea. Forecasting 23, 1032–1043 (2008)

    Article  Google Scholar 

  13. J. Zack, E.J. Natenberg, S. Young, G.V. Knowe, K. Waight, J. Manobianco, C. Kamath, Application of ensemble sensitivity analysis to observation targeting for short term wind speed forecasting in the Tehachapi region winter season, LLNL Technical Report LLNL-TR-460956 (2010)

    Google Scholar 

  14. J. Zack, E.J. Natenberg, S. Young, G.V. Knowe, K. Waight, J. Manobianco, C. Kamath, Application of ensemble sensitivity analysis to observation targeting for short term wind speed forecasting in the Washington-Oregon region, LLNL Technical Report LLNL-TR-458086 (2010)

    Google Scholar 

  15. J. Zack, E.J. Natenberg, S. Young, J. Manobianco, C. Kamath, Application of ensemble sensitivity analysis to observational targeting for short term wind speed forecasting, LLNL Technical Report LLNL-TR-424442 (2010)

    Google Scholar 

  16. J. Zhang, B.-M. Hodge, S. Lu, F.H. Hamann, B. Lehman, J. Simmons, E. Campos, V. Banunarayanan, Baseline and target values for PV forecasts: toward improved solar power forecasting, in Conference Paper NREL/CP-5D00-63876, Presented at the IEEE Power and Energy Society General Meeting, Denver, Colorado, 26–30 July 2015 (2015). http://www.nrel.gov/research/publications.html

  17. J. Zhang, Anthony Florita, Bri-Mathias Hodge, Lu Siyuan, Hendrik F. Hamann, Venkat Banunarayanan, Anna M. Brockway, A suite of metrics for assessing the performance of solar power forecasting. Sol. Energy 111, 157–175 (2015)

    Article  Google Scholar 

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Correspondence to John W. Zack .

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Zack, J.W. (2017). Wind and Solar Forecasting. In: Du, P., Baldick, R., Tuohy, A. (eds) Integration of Large-Scale Renewable Energy into Bulk Power Systems. Power Electronics and Power Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-55581-2_4

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  • DOI: https://doi.org/10.1007/978-3-319-55581-2_4

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