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A simple but accurate two-state model for nowcasting PV power

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  • Paulescu, Marius
  • Stefu, Nicoleta
  • Dughir, Ciprian
  • Sabadus, Andreea
  • Calinoiu, Delia
  • Badescu, Viorel

Abstract

A two-state model for short-term forecasting of PV plants output power (further referred as PV2-state) is proposed. PV2-state connects in an inventive way an empirical estimator for clear-sky photovoltaic (PV) power output with a statistical predictor for the sunshine number, a binary indicator stating whether the Sun shines or not. PV2-state shows remarkable features: (1) accessibility (only data series resulted from the PV plant monitoring are processed), (2) universality (no physical models for PV plant components are required) and (3) high-performance (due to a continuous adaptation to the actual atmospheric and PV modules conditions). Based on different error metrics, the model performance is investigated from three perspectives: forecast accuracy, forecast precision and the response to the variability in the state-of-the-sky. The study was conducted with high-quality data collected from a fully monitored experimental micro-PV plant developed on the Solar Platform of the West University of Timisoara, Romania. By processing information about the actual performance of the PV plant, PV2-state proves a notable advance in the forecast precision, becoming a robust competitor in the race for high-accuracy in intra-hour forecast of PV power.

Suggested Citation

  • Paulescu, Marius & Stefu, Nicoleta & Dughir, Ciprian & Sabadus, Andreea & Calinoiu, Delia & Badescu, Viorel, 2022. "A simple but accurate two-state model for nowcasting PV power," Renewable Energy, Elsevier, vol. 195(C), pages 322-330.
    • Handle: RePEc:eee:renene:v:195:y:2022:i:c:p:322-330
    DOI: 10.1016/j.renene.2022.05.056
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    References listed on IDEAS

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    1. Paulescu, Marius & Badescu, Viorel & Dughir, Ciprian, 2014. "New procedure and field-tests to assess photovoltaic module performance," Energy, Elsevier, vol. 70(C), pages 49-57.
    2. Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    3. Chien, Fengsheng & Hsu, Ching-Chi & Ozturk, Ilhan & Sharif, Arshian & Sadiq, Muhammad, 2022. "The role of renewable energy and urbanization towards greenhouse gas emission in top Asian countries: Evidence from advance panel estimations," Renewable Energy, Elsevier, vol. 186(C), pages 207-216.
    4. Elena Collino & Dario Ronzio, 2021. "Exploitation of a New Short-Term Multimodel Photovoltaic Power Forecasting Method in the Very Short-Term Horizon to Derive a Multi-Time Scale Forecasting System," Energies, MDPI, vol. 14(3), pages 1-30, February.
    5. Paulescu, Marius & Paulescu, Eugenia, 2019. "Short-term forecasting of solar irradiance," Renewable Energy, Elsevier, vol. 143(C), pages 985-994.
    6. Ueckerdt, Falko & Brecha, Robert & Luderer, Gunnar, 2015. "Analyzing major challenges of wind and solar variability in power systems," Renewable Energy, Elsevier, vol. 81(C), pages 1-10.
    7. Hassan, Muhammed A. & Bailek, Nadjem & Bouchouicha, Kada & Nwokolo, Samuel Chukwujindu, 2021. "Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks," Renewable Energy, Elsevier, vol. 171(C), pages 191-209.
    8. Kushwaha, Vishal & Pindoriya, Naran M., 2019. "A SARIMA-RVFL hybrid model assisted by wavelet decomposition for very short-term solar PV power generation forecast," Renewable Energy, Elsevier, vol. 140(C), pages 124-139.
    9. 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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    1. 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).

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