The temperature of the photovoltaic modules influences the electrical power output, as the temperature increases, the power extracted will decrease, other environmental conditions being equal. The degradation of PV modules over time is also influenced by the operating temperature and thermal cycling experienced by the modules. The estimation of the module temperature is therefore of crucial importance if it is not measurable due to the absence of sensors or for preliminary studies. In the present work, conventional models with machine learning regressors are compared for the estimation of the temperature of monofacial and bifacial photovoltaic modules installed in two different locations.
Thermal Models of Monofacial and Bifacial PV Modules: Machine Learning and Physical Estimation Models Comparison
Grisanti M.;Mannino G.;Tina G. M.;Ortis A.;Cacciato M.;Battiato S.;Canino A.
2023-01-01
Abstract
The temperature of the photovoltaic modules influences the electrical power output, as the temperature increases, the power extracted will decrease, other environmental conditions being equal. The degradation of PV modules over time is also influenced by the operating temperature and thermal cycling experienced by the modules. The estimation of the module temperature is therefore of crucial importance if it is not measurable due to the absence of sensors or for preliminary studies. In the present work, conventional models with machine learning regressors are compared for the estimation of the temperature of monofacial and bifacial photovoltaic modules installed in two different locations.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
