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Tools for PV (photovoltaic) plant operators: Nowcasting of passing clouds

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  • Paulescu, Marius
  • Badescu, Viorel
  • Brabec, Marek

Abstract

The response time of a PV (photovoltaic) plant is very short and its output power follows the abrupt change in solar irradiance level due to alternate shadow by clouds. The sunshine number (SSN) is a Boolean quantity stating whether the sun is covered by clouds or not, thus being an appropriate parameter to predict the occurrence of direct solar radiation at ground level. Various ARIMA (Autoregressive Integrated Moving Average) models for SSN nowcasting are inferred and discussed in this paper. Actinometric and meteorological data measured at 15 s lag during June 2010 in Timisoara (Romania) are used. The forecasting accuracy is studied as a function of season, of the procedure used to obtain a binary time series and of the type of white noise distribution, respectively. It is demonstrated that the ARIMA(0,1,0) model forecasts SSN with the same accuracy as higher order ARIMA models. The forecasting accuracy decreases when the instability of the radiative regime increases.

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  • Paulescu, Marius & Badescu, Viorel & Brabec, Marek, 2013. "Tools for PV (photovoltaic) plant operators: Nowcasting of passing clouds," Energy, Elsevier, vol. 54(C), pages 104-112.
    • Handle: RePEc:eee:energy:v:54:y:2013:i:c:p:104-112
    DOI: 10.1016/j.energy.2013.03.005
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    References listed on IDEAS

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    1. Voyant, Cyril & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure, 2011. "Optimization of an artificial neural network dedicated to the multivariate forecasting of daily global radiation," Energy, Elsevier, vol. 36(1), pages 348-359.
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    6. Tarroja, Brian & Mueller, Fabian & Eichman, Joshua D. & Samuelsen, Scott, 2012. "Metrics for evaluating the impacts of intermittent renewable generation on utility load-balancing," Energy, Elsevier, vol. 42(1), pages 546-562.
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    Citations

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    Cited by:

    1. Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2015. "Nearest-neighbor methodology for prediction of intra-hour global horizontal and direct normal irradiances," Renewable Energy, Elsevier, vol. 80(C), pages 770-782.
    2. Marek Brabec & Viorel Badescu & Marius Paulescu, 2014. "Cloud shade by dynamic logistic modeling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(6), pages 1174-1188, June.
    3. Paulescu, Marius & Paulescu, Eugenia, 2019. "Short-term forecasting of solar irradiance," Renewable Energy, Elsevier, vol. 143(C), pages 985-994.
    4. Savvakis, Nikolaos & Tsoutsos, Theocharis, 2015. "Performance assessment of a thin film photovoltaic system under actual Mediterranean climate conditions in the island of Crete," Energy, Elsevier, vol. 90(P2), pages 1435-1455.
    5. Paulescu, Marius & Blaga, Robert & Dughir, Ciprian & Stefu, Nicoleta & Sabadus, Andreea & Calinoiu, Delia & Badescu, Viorel, 2023. "Intra-hour PV power forecasting based on sky imagery," Energy, Elsevier, vol. 279(C).
    6. da Silva Fonseca Junior, Joao Gari & Oozeki, Takashi & Ohtake, Hideaki & Shimose, Ken-ichi & Takashima, Takumi & Ogimoto, Kazuhiko, 2014. "Regional forecasts and smoothing effect of photovoltaic power generation in Japan: An approach with principal component analysis," Renewable Energy, Elsevier, vol. 68(C), pages 403-413.

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