Assessing Evidence for Weather Regimes Governing Solar Power Generation in Kuwait

Mari R. Tye, Sue Ellen Haupt, Eric Gilleland, Christina Kalb and Tara Jensen
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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

Energies, 2019, vol. 12, issue 23, 1-17

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.

Keywords: mixture distribution; sky clearness; solar radiation classification; weather patterns (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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