Application of adaptive models for the determination of the thermal behaviour of a photovoltaic panel

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2 Citazioni (Scopus)

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-electricbehaviour 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.
Lingua originaleEnglish
Numero di pagine15
Stato di pubblicazionePublished - 2013

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Neural Networks
Neural networks
Test facilities
Forecasting
Economics
Temperature
Necessary
Evaluation
Energy
Modeling
Model
Simulation
Hot Temperature
Influence

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cita questo

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title = "Application of adaptive models for the determination of the thermal behaviour of a photovoltaic panel",
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-electricbehaviour 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.",
author = "{Lo Brano}, Valerio and Giuseppina Ciulla and Marco Beccali",
year = "2013",
language = "English",

}

TY - CONF

T1 - Application of adaptive models for the determination of the thermal behaviour of a photovoltaic panel

AU - Lo Brano, Valerio

AU - Ciulla, Giuseppina

AU - Beccali, Marco

PY - 2013

Y1 - 2013

N2 - 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-electricbehaviour 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.

AB - 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-electricbehaviour 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.

UR - http://hdl.handle.net/10447/76552

M3 - Other

ER -