Open AccessApplication of an adaptive Bayesian‐based model for probabilistic and deterministic PV forecasting
Author(s) -
Abedinia Oveis,
Bagheri Mehdi,
Agelidis Vassilios G.
Publication year - 2021
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/rpg2.12194
Subject(s) - probabilistic logic , probabilistic forecasting , computer science , photovoltaic system , gaussian , energy (signal processing) , bayesian inference , solar energy , bayesian probability , statistical model , mathematical optimization , algorithm , machine learning , artificial intelligence , mathematics , statistics , engineering , physics , quantum mechanics , electrical engineering
Abstract Accurate prediction of solar photovoltaic plant energy generation is essential for optimal planning and operation of modern power systems, and incorporating such plants into the energy sector. In this study, an adaptive Gaussian mixture method (AGM) and a developed variational Bayesian model (VBM) inference through multikernel regression (MkR) are utilized to assist desirable precise prediction. In this model, the MkR processes the multiresolution solar energy signal, and then the AGM models the complex signals forecasting error. Finally, the proposed model can be optimized, and the concurrent output of the solar energy signal in both probabilistic and deterministic status can be attained through the introduction of the VBM. The solar energy output of an actual plant, including four measurement sites provided the data for the study. The results confirmed that the proposed model delivers higher prediction accuracy for both probabilistic and deterministic forecasts when compared with other well‐known models.