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A spatiotemporal auto-regressive moving average model for solar radiation

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  • C. A. Glasbey
  • D. J. Allcroft

Abstract

To investigate the variability in energy output from a network of photovoltaic cells, solar radiation was recorded at 10 sites every 10 min in the Pentland Hills to the south of Edinburgh. We identify spatiotemporal auto-regressive moving average models as the most appropriate to address this problem. Although previously considered computationally prohibitive to work with, we show that by approximating using toroidal space and fitting by matching auto-correlations, calculations can be substantially reduced. We find that a first-order spatiotemporal auto-regressive (STAR(1)) process with a first-order neighbourhood structure and a Matern noise process provide an adequate fit to the data, and we demonstrate its use in simulating realizations of energy output. Copyright (c) 2008 Biomathematics and Statistics, Scotland Journal compilation (c) 2008 Royal Statistical Society.

Suggested Citation

  • C. A. Glasbey & D. J. Allcroft, 2008. "A spatiotemporal auto-regressive moving average model for solar radiation," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(3), pages 343-355.
  • Handle: RePEc:bla:jorssc:v:57:y:2008:i:3:p:343-355
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    File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-9876.2007.00617.x
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    Cited by:

    1. Patrick, Joshua D. & Harvill, Jane L. & Hansen, Clifford W., 2016. "A semiparametric spatio-temporal model for solar irradiance data," Renewable Energy, Elsevier, vol. 87(P1), pages 15-30.
    2. repec:eee:appene:v:211:y:2018:i:c:p:15-27 is not listed on IDEAS

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