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Intra-day solar irradiation forecast using RLS filters and satellite images

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  • Marchesoni-Acland, Franco
  • Alonso-Suárez, Rodrigo

Abstract

Satellite-based solar irradiation forecasting is useful for short-term intra-day time horizons, outperforming numerical weather predictions up to 3–4 h ahead. The main techniques for solar satellite forecast are based on sophisticated cloud motion estimates from geostationary satellite images. This work explores the use of satellite information in a simpler way, namely spatial averages that require almost no preprocessing. Adaptive auto-regressive models are used to assess the impact of this information on the forecasting performance. A complete analysis regarding model selection, the satellite averaging window size and the inclusion of satellite past measurements is provided. It is shown that: (i) satellite spatial averages are useful inputs and the averaging window size is an important parameter, (ii) satellite lags are of limited utility and spatial averages are more useful than weighted time averages, and (iii) there is no value in fine-tuning the orders of auto-regressive models for each time horizon, as the same performance can be obtained by using a fixed well-selected order. These ideas are tested for a region that has intermediate solar variability, and the models succeed to outperform a proposed optimal smart persistence, used here as an exigent performance benchmark.

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  • Marchesoni-Acland, Franco & Alonso-Suárez, Rodrigo, 2020. "Intra-day solar irradiation forecast using RLS filters and satellite images," Renewable Energy, Elsevier, vol. 161(C), pages 1140-1154.
    • Handle: RePEc:eee:renene:v:161:y:2020:i:c:p:1140-1154
    DOI: 10.1016/j.renene.2020.07.101
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    1. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    2. Dambreville, Romain & Blanc, Philippe & Chanussot, Jocelyn & Boldo, Didier, 2014. "Very short term forecasting of the Global Horizontal Irradiance using a spatio-temporal autoregressive model," Renewable Energy, Elsevier, vol. 72(C), pages 291-300.
    3. Yagli, Gokhan Mert & Yang, Dazhi & Srinivasan, Dipti, 2019. "Automatic hourly solar forecasting using machine learning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 487-498.
    4. Voyant, Cyril & Notton, Gilles, 2018. "Solar irradiation nowcasting by stochastic persistence: A new parsimonious, simple and efficient forecasting tool," Renewable and Sustainable Energy Reviews, Elsevier, vol. 92(C), pages 343-352.
    5. Aguiar, L. Mazorra & Pereira, B. & Lauret, P. & Díaz, F. & David, M., 2016. "Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting," Renewable Energy, Elsevier, vol. 97(C), pages 599-610.
    6. Diagne, Maimouna & David, Mathieu & Lauret, Philippe & Boland, John & Schmutz, Nicolas, 2013. "Review of solar irradiance forecasting methods and a proposition for small-scale insular grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 65-76.
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    3. Qiangsheng Bu & Shuyi Zhuang & Fei Luo & Zhigang Ye & Yubo Yuan & Tianrui Ma & Tao Da, 2024. "Improving Solar Radiation Forecasting in Cloudy Conditions by Integrating Satellite Observations," Energies, MDPI, vol. 17(24), pages 1-20, December.

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