A Hybrid Technique for Day-Ahead PV Generation Forecasting Using Clear-Sky Models or Ensemble of Artificial Neural Networks According to a Decision Tree Approach

Autor:	Stefano Massucco, Gabriele Mosaico, Matteo Saviozzi, Federico Silvestro
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	PV forecasting hybrid method clear-sky model artificial neural networks basic ensemble method decision trees CART tree weather type partition weather classification Technology
Zdroj:	Energies, Vol 12, Iss 7, p 1298 (2019)
Druh dokumentu:	article
ISSN:	1996-1073
DOI:	10.3390/en12071298
Popis:	PhotoVoltaic (PV) plants can provide important economic and environmental benefits to electric systems. On the other hand, the variability of the solar source leads to technical challenges in grid management as PV penetration rates increase continuously. For this reason, PV power forecasting represents a crucial tool for uncertainty management to ensure system stability. In this paper, a novel hybrid methodology for the PV forecasting is presented. The proposed approach can exploit clear-sky models or an ensemble of artificial neural networks, according to day-ahead weather forecast. In particular, the selection among these techniques is performed through a decision tree approach, which is designed to choose the best method among those aforementioned. The presented methodology has been validated on a real PV plant with very promising results.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/acffc2beeef440baa6fead85d91355d3 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.