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

Karol Rosen, César Angeles-Camacho, Víctor Elvira and Servio Tulio Guillén-Burguete

Energy, 2023, vol. 278, issue PB

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.

Keywords: Photovoltaic energy; Forecasting; Markov switching models; All-sky images; Clustering (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:278:y:2023:i:pb:s0360544223013464

DOI: 10.1016/j.energy.2023.127952

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