Forecasting the Cell Temperature of PV Modules with an Adaptive System
The need to reduce energy consumptions and to optimize the processes of energy production has pushed the technology towards the implementation of hybrid systems for combined production of electric and thermal energies. In particular, recent researches look with interest at the installation of hybrid...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2013/192854 |
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| author | Giuseppina Ciulla Valerio Lo Brano Edoardo Moreci |
| author_facet | Giuseppina Ciulla Valerio Lo Brano Edoardo Moreci |
| author_sort | Giuseppina Ciulla |
| collection | DOAJ |
| description | The need to reduce energy consumptions and to optimize the processes of energy production has pushed the technology towards the implementation of hybrid systems for combined production of electric and thermal energies. In particular, recent researches look with interest at the installation of hybrid system PV/T. To improve the energy performance of these systems, it is necessary to know the operating temperature of the photovoltaic modules. The determination of the operating temperature is a key parameter for the assessment of the actual performance of photovoltaic panels. In the literature, it is possible to find different correlations that evaluate the referring to standard test conditions and/or applying some theoretical simplifications/assumptions. Nevertheless, the application of these different correlations, for the same conditions, does not lead to unequivocal results. In this work an alternative method, based on the employment of artificial neural networks (ANNs), was proposed to predict the operating temperature of a PV module. This methodology does not require any simplification or physical assumptions. In the paper is described the ANN that obtained the best performance: a multilayer perception network. The results have been compared with experimental monitored data and with some of the most cited empirical correlations proposed by different authors. |
| format | Article |
| id | doaj-art-2475aacf3ef7442d93ff18e39cf3d0e8 |
| institution | Kabale University |
| issn | 1110-662X 1687-529X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-2475aacf3ef7442d93ff18e39cf3d0e82025-02-03T01:28:00ZengWileyInternational Journal of Photoenergy1110-662X1687-529X2013-01-01201310.1155/2013/192854192854Forecasting the Cell Temperature of PV Modules with an Adaptive SystemGiuseppina Ciulla0Valerio Lo Brano1Edoardo Moreci2DEIM, University of Palermo, Viale delle Scienze, Edificio 9, 90128 Palermo, ItalyDEIM, University of Palermo, Viale delle Scienze, Edificio 9, 90128 Palermo, ItalyDEIM, University of Palermo, Viale delle Scienze, Edificio 9, 90128 Palermo, ItalyThe need to reduce energy consumptions and to optimize the processes of energy production has pushed the technology towards the implementation of hybrid systems for combined production of electric and thermal energies. In particular, recent researches look with interest at the installation of hybrid system PV/T. To improve the energy performance of these systems, it is necessary to know the operating temperature of the photovoltaic modules. The determination of the operating temperature is a key parameter for the assessment of the actual performance of photovoltaic panels. In the literature, it is possible to find different correlations that evaluate the referring to standard test conditions and/or applying some theoretical simplifications/assumptions. Nevertheless, the application of these different correlations, for the same conditions, does not lead to unequivocal results. In this work an alternative method, based on the employment of artificial neural networks (ANNs), was proposed to predict the operating temperature of a PV module. This methodology does not require any simplification or physical assumptions. In the paper is described the ANN that obtained the best performance: a multilayer perception network. The results have been compared with experimental monitored data and with some of the most cited empirical correlations proposed by different authors.http://dx.doi.org/10.1155/2013/192854 |
| spellingShingle | Giuseppina Ciulla Valerio Lo Brano Edoardo Moreci Forecasting the Cell Temperature of PV Modules with an Adaptive System International Journal of Photoenergy |
| title | Forecasting the Cell Temperature of PV Modules with an Adaptive System |
| title_full | Forecasting the Cell Temperature of PV Modules with an Adaptive System |
| title_fullStr | Forecasting the Cell Temperature of PV Modules with an Adaptive System |
| title_full_unstemmed | Forecasting the Cell Temperature of PV Modules with an Adaptive System |
| title_short | Forecasting the Cell Temperature of PV Modules with an Adaptive System |
| title_sort | forecasting the cell temperature of pv modules with an adaptive system |
| url | http://dx.doi.org/10.1155/2013/192854 |
| work_keys_str_mv | AT giuseppinaciulla forecastingthecelltemperatureofpvmoduleswithanadaptivesystem AT valeriolobrano forecastingthecelltemperatureofpvmoduleswithanadaptivesystem AT edoardomoreci forecastingthecelltemperatureofpvmoduleswithanadaptivesystem |