The use of reliable forecasting models for the PV temperature is necessary for a more correct evaluation of energy and economic performances. Climatic conditions certainly have a remarkable influence on thermo-electric behaviour of the PV panel but the physical system is too complex for an analytical representation. A neural-network-based approach for solar panel temperature modelling is here presented. The models were trained using a set of data collected from a test facility. Simulation results of the trained neural networks are presented and compared with those obtained with an empirical correlation.
lo brano, v., ciulla, g., beccali, m. (2013). Application of adaptive models for the determination of the thermal behaviour of a photovoltaic panel. In Springer LNCS ICCSA 2013 Proceedings.
Application of adaptive models for the determination of the thermal behaviour of a photovoltaic panel
LO BRANO, Valerio;CIULLA, Giuseppina;BECCALI, Marco
2013-01-01
Abstract
The use of reliable forecasting models for the PV temperature is necessary for a more correct evaluation of energy and economic performances. Climatic conditions certainly have a remarkable influence on thermo-electric behaviour of the PV panel but the physical system is too complex for an analytical representation. A neural-network-based approach for solar panel temperature modelling is here presented. The models were trained using a set of data collected from a test facility. Simulation results of the trained neural networks are presented and compared with those obtained with an empirical correlation.File | Dimensione | Formato | |
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