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Intra-hour PV power forecasting based on sky imagery

Author

Listed:
  • Paulescu, Marius
  • Blaga, Robert
  • Dughir, Ciprian
  • Stefu, Nicoleta
  • Sabadus, Andreea
  • Calinoiu, Delia
  • Badescu, Viorel

Abstract

This paper introduces an upgraded version of the PV2-state model [Paulescu et al. Renew Energy 195 (2022) 322] for intra-hour photovoltaic (PV) power forecasting. The model incorporates real-time adjustments to the estimated clear-sky PV power or the transmittance of clouds, considering the Sun coverage by clouds. A physics-based approach for processing cloud field information from an all-sky imager is proposed to intra-hour forecast SSN, a binary quantifier stating whether the Sun is shining (SSN = 1) or not (SSN = 0). The model performance with the new procedure 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 1-min data collected from a micro-PV plant, on a sample of days with scattered clouds. PV2-state demonstrates notable performance particularly under challenging conditions, where models typically struggle to perform well. In terms of skill score, the overall model performance ranges between 10 and 20%, and under very high variability conditions even exceeding four times the purely statistical forecast. For the operationally relevant 15-min horizon, PV2-state exhibits a noteworthy precision, with two-thirds of forecasts falling within a 10% tolerance interval. Even under severe conditions roughly one third falls in this 10% interval.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:279:y:2023:i:c:s0360544223015293
    DOI: 10.1016/j.energy.2023.128135
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    References listed on IDEAS

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