Markov-Switching Models for Probabilistic Solar Resource Assessment and Forecasting
Author | : Vivek Srikrishnan |
Publisher | : |
Total Pages | : |
Release | : 2018 |
Genre | : |
ISBN | : |
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This work proposes and analyzes a Markov-switching autoregression model structurefor joint probabilistic modeling of the beam and global components of solarirradiance, which are important to simulate the performance of a variety of solarenergy conversion devices, including solar photovoltaics. The ability of this modelto assess the hourly solar resource is tested, using both a version of the modelthat is calibrated using all-year data and a version of the model that combinesindividual seasonally-calibrated models. While this simple model does not fullycapture the behavior of the solar resource, an analysis of the posterior predictivedistribution reveals strategies for improvement. A version of this model is also usedto forecast both irradiance components for a 15-minute lead time while assimilatinggeostationary satellite data into an inhomogeneous transition probability specification.The inhomogeneous specifications produce sharper predictive distributionsthan an analogous homogeneous model, but all have similar skill relative to a smartpersistence forecast.