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Assessing Evidence for Weather Regimes Governing Solar Power Generation in Kuwait

Author

Listed:
  • Mari R. Tye

    (National Center for Atmospheric Research, Boulder, CO 80307, USA)

  • Sue Ellen Haupt

    (National Center for Atmospheric Research, Boulder, CO 80307, USA)

  • Eric Gilleland

    (National Center for Atmospheric Research, Boulder, CO 80307, USA)

  • Christina Kalb

    (National Center for Atmospheric Research, Boulder, CO 80307, USA)

  • Tara Jensen

    (National Center for Atmospheric Research, Boulder, CO 80307, USA)

Abstract

With electricity representing around 20% of the global energy demand, and increasing support for renewable sources of electricity, there is also an escalating need to improve solar forecasts to support power management. While considerable research has been directed to statistical methods to improve solar power forecasting, few have employed finite mixture distributions. A statistically-objective classification of the overall sky condition may lead to improved forecasts. Combining information from the synoptic driving conditions for daily variability with local processes controlling subdaily fluctuations could assist with forecast validation and enhancement where few observations are available. Gaussian mixture models provide a statistical learning approach to automatically identify prevalent sky conditions (clear, semi-cloudy, and cloudy) and explore associated weather patterns. Here a first stage in the development of such a model is presented: examining whether there is sufficient information in the large-scale environment to identify days with clear, semi-cloudy, or cloudy conditions. A three-component Gaussian distribution is developed that reproduces the observed multimodal peaks in sky clearness indices, and their temporal distribution. Posterior probabilities from the fitted mixture distributions are used to identify periods of clear, partially-cloudy, and cloudy skies. Composites of low-level (850 hPa) humidity and winds for each of the mixture components reveal three patterns associated with the typical synoptic conditions governing the sky clarity, and hence, potential solar power.

Suggested Citation

  • Mari R. Tye & Sue Ellen Haupt & Eric Gilleland & Christina Kalb & Tara Jensen, 2019. "Assessing Evidence for Weather Regimes Governing Solar Power Generation in Kuwait," Energies, MDPI, vol. 12(23), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:23:p:4409-:d:288886
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    References listed on IDEAS

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    1. Reikard, Gordon & Haupt, Sue Ellen & Jensen, Tara, 2017. "Forecasting ground-level irradiance over short horizons: Time series, meteorological, and time-varying parameter models," Renewable Energy, Elsevier, vol. 112(C), pages 474-485.
    2. Benaglia, Tatiana & Chauveau, Didier & Hunter, David R. & Young, Derek S., 2009. "mixtools: An R Package for Analyzing Mixture Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i06).
    3. Antonio Bracale & Pasquale De Falco, 2015. "An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power," Energies, MDPI, vol. 8(9), pages 1-22, September.
    4. B. Béranger & T. Duong & S. E. Perkins-Kirkpatrick & S. A. Sisson, 2019. "Tail density estimation for exploratory data analysis using kernel methods," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 31(1), pages 144-174, January.
    5. Hocaoglu, Fatih Onur & Serttas, Fatih, 2017. "A novel hybrid (Mycielski-Markov) model for hourly solar radiation forecasting," Renewable Energy, Elsevier, vol. 108(C), pages 635-643.
    6. Zhang, Jie & Chowdhury, Souma & Messac, Achille & Castillo, Luciano, 2013. "A Multivariate and Multimodal Wind Distribution model," Renewable Energy, Elsevier, vol. 51(C), pages 436-447.
    7. Deshmukh, M.K. & Deshmukh, S.S., 2008. "Modeling of hybrid renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(1), pages 235-249, January.
    8. van der Meer, D.W. & Widén, J. & Munkhammar, J., 2018. "Review on probabilistic forecasting of photovoltaic power production and electricity consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1484-1512.
    9. McCandless, T.C. & Haupt, S.E. & Young, G.S., 2016. "A regime-dependent artificial neural network technique for short-range solar irradiance forecasting," Renewable Energy, Elsevier, vol. 89(C), pages 351-359.
    10. Köhler, Carmen & Steiner, Andrea & Saint-Drenan, Yves-Marie & Ernst, Dominique & Bergmann-Dick, Anja & Zirkelbach, Mathias & Ben Bouallègue, Zied & Metzinger, Isabel & Ritter, Bodo, 2017. "Critical weather situations for renewable energies – Part B: Low stratus risk for solar power," Renewable Energy, Elsevier, vol. 101(C), pages 794-803.
    11. Diagne, Maimouna & David, Mathieu & Lauret, Philippe & Boland, John & Schmutz, Nicolas, 2013. "Review of solar irradiance forecasting methods and a proposition for small-scale insular grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 65-76.
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    Cited by:

    1. Sue Ellen Haupt & Tyler C. McCandless & Susan Dettling & Stefano Alessandrini & Jared A. Lee & Seth Linden & William Petzke & Thomas Brummet & Nhi Nguyen & Branko Kosović & Gerry Wiener & Tahani Hussa, 2020. "Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting," Energies, MDPI, vol. 13(8), pages 1-23, April.
    2. Naegele, S.M. & McCandless, T.C. & Greybush, S.J. & Young, G.S. & Haupt, S.E. & Al-Rasheedi, M., 2020. "Climatology of wind variability for the Shagaya region in Kuwait," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).

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