Realtime Decomposition of Site-Measured Solar Irradiance Using Machine Learning for Bifacial System Performance Characterization

Autor:	John M. Obrecht, Zoe Defreitas, Aron Dobos
Rok vydání:	2021
Předmět:	Pyranometer business.industry Photovoltaic system Irradiance Machine learning computer.software_genre Solar irradiance View factor Software Solar power plant Decomposition (computer science) Environmental science Artificial intelligence business computer
Zdroj:	2021 IEEE 48th Photovoltaic Specialists Conference (PVSC).
DOI:	10.1109/pvsc43889.2021.9518419
Popis:	This work presents a method for decomposing realtime on-site measured plane of array (POA) and global horizontal (GHI) irradiance from pyranometers into beam and diffuse components. A machine learning method is applied in conjunction with typical metereological year (TMY) irradiance data to facilitate reliable irradiance decomposition of five minute measured data, a regime in which conventional decomposition methods like GTI-DIRINT sometimes struggle. The approach is combined with view factor models of rear side irradiance for bifacial systems to reliably calculate performance ratio and other metrics. Validation of the method on bifacial utility-scale solar power plant data shows credible results.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::5b7dc375c5844eccd45f051a6fed5406 https://doi.org/10.1109/pvsc43889.2021.9518419 Zobrazit plný text záznamu