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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

Summary. 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.

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  • 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, June.
    • Handle: RePEc:bla:jorssc:v:57:y:2008:i:3:p:343-355
    DOI: 10.1111/j.1467-9876.2007.00617.x
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    Cited by:

    1. Bouzgou, Hassen & Gueymard, Christian A., 2019. "Fast short-term global solar irradiance forecasting with wrapper mutual information," Renewable Energy, Elsevier, vol. 133(C), pages 1055-1065.
    2. Lan, Hai & Zhang, Chi & Hong, Ying-Yi & He, Yin & Wen, Shuli, 2019. "Day-ahead spatiotemporal solar irradiation forecasting using frequency-based hybrid principal component analysis and neural network," Applied Energy, Elsevier, vol. 247(C), pages 389-402.
    3. Wenqi Zhang & William Kleiber & Bri‐Mathias Hodge & Barry Mather, 2022. "A nonstationary and non‐Gaussian moving average model for solar irradiance," Environmetrics, John Wiley & Sons, Ltd., vol. 33(3), May.
    4. 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.
    5. Lan, Hai & Yin, He & Hong, Ying-Yi & Wen, Shuli & Yu, David C. & Cheng, Peng, 2018. "Day-ahead spatio-temporal forecasting of solar irradiation along a navigation route," Applied Energy, Elsevier, vol. 211(C), pages 15-27.
    6. Fateh Mehazzem & Maina André & Rudy Calif, 2022. "Efficient Output Photovoltaic Power Prediction Based on MPPT Fuzzy Logic Technique and Solar Spatio-Temporal Forecasting Approach in a Tropical Insular Region," Energies, MDPI, vol. 15(22), pages 1-21, November.
    7. Luisa Ferrari & Giuseppe Gerardi & Giancarlo Manzi & Alessandra Micheletti & Federica Nicolussi & Elia Biganzoli & Silvia Salini, 2021. "Modeling Provincial Covid-19 Epidemic Data Using an Adjusted Time-Dependent SIRD Model," IJERPH, MDPI, vol. 18(12), pages 1-20, June.

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