What is it about?

This model assumes that the sky is in one of three cloudiness states: cloudy, clear, and scattered clouds. The model then learns the probabilities of jumping between these cloudiness states. After learning these probabilities an infinite number of time-series can be generated to simulate the solar yield in a location.

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Why is it important?

This model formally connected the physical and mathematical representations of the clear-sky models, which was not done previously.


It was a simple yet novel application of the hidden Markov models.

Mahmoud Shepero
Uppsala Universitet

Read the Original

This page is a summary of: A generative hidden Markov model of the clear-sky index, Journal of Renewable and Sustainable Energy, July 2019, American Institute of Physics,
DOI: 10.1063/1.5110785.
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