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Intra-hour photovoltaic forecasting through a time-varying Markov switching model

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Listed:
  • Rosen, Karol
  • Angeles-Camacho, César
  • Elvira, Víctor
  • Guillén-Burguete, Servio Tulio

Abstract

This work presents a Markov switching model with a time-varying transition matrix to forecast intra-hour photovoltaic (PV) power output, aiming at providing forecasting flexibility. First, the proposed methodology captures images of the sky employing a self-made, low-cost all-sky imager. Second, the goal is to limit exposure problems in those images via the exposure fusion technique. Third, the proposed algorithm identifies groups of pixels forming clouds through a super paramagnetic clustering algorithm. Finally, we model the problem with a homogeneous Poisson process and forecast the cloud location and the shadowed area on a PV plant for the coming minutes. The shadowed area together with meteorological data are the inputs to this model. In the case study, our approach shows better performance than the persistence method, in particular for changing cloud conditions.

Suggested Citation

  • Rosen, Karol & Angeles-Camacho, César & Elvira, Víctor & Guillén-Burguete, Servio Tulio, 2023. "Intra-hour photovoltaic forecasting through a time-varying Markov switching model," Energy, Elsevier, vol. 278(PB).
    • Handle: RePEc:eee:energy:v:278:y:2023:i:pb:s0360544223013464
    DOI: 10.1016/j.energy.2023.127952
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