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<dc:title>Analysis and Prediction of Energy Production in Concentrating Photovoltaic (CPV) Installations</dc:title>
<dc:creator>Gómez Gil, Francisco Javier</dc:creator>
<dc:creator>Xiaoting, Wang</dc:creator>
<dc:creator>Barnett, Allen</dc:creator>
<dc:subject>concentrating photovoltaics</dc:subject>
<dc:subject>CPV</dc:subject>
<dc:subject>energy production</dc:subject>
<dc:subject>prediction</dc:subject>
<dc:subject>analysis</dc:subject>
<dc:subject>Ingeniería mecánica</dc:subject>
<dc:subject>Mechanical engineering</dc:subject>
<dc:description>A method for the prediction of Energy Production (EP) in Concentrating&#xd;
Photovoltaic (CPV) installations is examined in this study. It presents a new method that&#xd;
predicts EP by using Global Horizontal Irradiation (GHI) and the Photovoltaic&#xd;
Geographical Information System (PVGIS) database, instead of Direct Normal Irradiation&#xd;
(DNI) data, which are rarely recorded at most locations. EP at four Spanish CPV&#xd;
installations is analyzed: two are based on silicon solar cells and the other two on&#xd;
multi-junction III-V solar cells. The real EP is compared with the predicted EP. Two&#xd;
methods for EP prediction are presented. In the first preliminary method, a monthly&#xd;
Performance Ratio (PR) is used as an arbitrary constant value (75%) and an estimation of&#xd;
the DNI. The DNI estimation is obtained from GHI measurements and the PVGIS&#xd;
database. In the second method, a lineal model is proposed for the first time in this paper to&#xd;
obtain the predicted EP from the estimated DNI. This lineal model is the regression line&#xd;
that correlates the real monthly EP and the estimated DNI in 2009. This new method&#xd;
implies that the monthly PR is variable. Using the new method, the difference between the&#xd;
predicted and the real EP values is less than 2% for the annual EP and is in the range of&#xd;
5.6%–16.1% for the monthly EP. The method that uses the variable monthly PR allows the&#xd;
prediction of the EP with reasonable accuracy. It is therefore possible to predict the CPV&#xd;
EP for any location, using only widely available GHI data and the PVGIS database.</dc:description>
<dc:date>2017-03-17T09:12:53Z</dc:date>
<dc:date>2017-03-17T09:12:53Z</dc:date>
<dc:date>2012-03</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
<dc:identifier>1996-1073</dc:identifier>
<dc:identifier>http://hdl.handle.net/10259/4374</dc:identifier>
<dc:identifier>10.3390/en5030770</dc:identifier>
<dc:language>eng</dc:language>
<dc:relation>Energies, 2012, V. 5, n. 3, p. 770-789</dc:relation>
<dc:relation>http://dx.doi.org/10.3390/en5030770</dc:relation>
<dc:rights>Attribution 3.0 Unported</dc:rights>
<dc:rights>http://creativecommons.org/licenses/by/3.0/</dc:rights>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:format>application/pdf</dc:format>
<dc:publisher>MDPI</dc:publisher>
<europeana:object>https://riubu.ubu.es/bitstream/10259/4374/7/G%c3%b3mez-Energies_2012.pdf.jpg</europeana:object>
<europeana:provider>Hispana</europeana:provider>
<europeana:type>TEXT</europeana:type>
<europeana:rights>http://creativecommons.org/licenses/by/3.0/</europeana:rights>
<europeana:dataProvider>RIUBU. Repositorio Institucional de la Universidad de Burgos</europeana:dataProvider>
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