Analysis and Prediction of Energy Production in Concentrating Photovoltaic (CPV) Installations
A method for the prediction of Energy Production (EP) in Concentrating Photovoltaic (CPV) installations is examined in this study. It presents a new method that predicts EP by using Global Horizontal Irradiation (GHI) and the Photovoltaic Geographical Information System (PVGIS) database, instead of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2012-03-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/5/3/770/ |
| _version_ | 1798043548390522880 |
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| author | Allen Barnett Xiaoting Wang Francisco J. Gómez-Gil |
| author_facet | Allen Barnett Xiaoting Wang Francisco J. Gómez-Gil |
| author_sort | Allen Barnett |
| collection | DOAJ |
| description | A method for the prediction of Energy Production (EP) in Concentrating Photovoltaic (CPV) installations is examined in this study. It presents a new method that predicts EP by using Global Horizontal Irradiation (GHI) and the Photovoltaic Geographical Information System (PVGIS) database, instead of Direct Normal Irradiation (DNI) data, which are rarely recorded at most locations. EP at four Spanish CPV installations is analyzed: two are based on silicon solar cells and the other two on multi-junction III-V solar cells. The real EP is compared with the predicted EP. Two methods for EP prediction are presented. In the first preliminary method, a monthly Performance Ratio (PR) is used as an arbitrary constant value (75%) and an estimation of the DNI. The DNI estimation is obtained from GHI measurements and the PVGIS database. In the second method, a lineal model is proposed for the first time in this paper to obtain the predicted EP from the estimated DNI. This lineal model is the regression line that correlates the real monthly EP and the estimated DNI in 2009. This new method implies that the monthly PR is variable. Using the new method, the difference between the predicted and the real EP values is less than 2% for the annual EP and is in the range of 5.6%–16.1% for the monthly EP. The method that uses the variable monthly PR allows the prediction of the EP with reasonable accuracy. It is therefore possible to predict the CPV EP for any location, using only widely available GHI data and the PVGIS database. |
| first_indexed | 2024-04-11T22:50:37Z |
| format | Article |
| id | doaj.art-23bffad511b94949baadd8dd9cd26645 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T22:50:37Z |
| publishDate | 2012-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-23bffad511b94949baadd8dd9cd266452022-12-22T03:58:35ZengMDPI AGEnergies1996-10732012-03-015377078910.3390/en5030770Analysis and Prediction of Energy Production in Concentrating Photovoltaic (CPV) InstallationsAllen BarnettXiaoting WangFrancisco J. Gómez-GilA method for the prediction of Energy Production (EP) in Concentrating Photovoltaic (CPV) installations is examined in this study. It presents a new method that predicts EP by using Global Horizontal Irradiation (GHI) and the Photovoltaic Geographical Information System (PVGIS) database, instead of Direct Normal Irradiation (DNI) data, which are rarely recorded at most locations. EP at four Spanish CPV installations is analyzed: two are based on silicon solar cells and the other two on multi-junction III-V solar cells. The real EP is compared with the predicted EP. Two methods for EP prediction are presented. In the first preliminary method, a monthly Performance Ratio (PR) is used as an arbitrary constant value (75%) and an estimation of the DNI. The DNI estimation is obtained from GHI measurements and the PVGIS database. In the second method, a lineal model is proposed for the first time in this paper to obtain the predicted EP from the estimated DNI. This lineal model is the regression line that correlates the real monthly EP and the estimated DNI in 2009. This new method implies that the monthly PR is variable. Using the new method, the difference between the predicted and the real EP values is less than 2% for the annual EP and is in the range of 5.6%–16.1% for the monthly EP. The method that uses the variable monthly PR allows the prediction of the EP with reasonable accuracy. It is therefore possible to predict the CPV EP for any location, using only widely available GHI data and the PVGIS database.http://www.mdpi.com/1996-1073/5/3/770/concentrating photovoltaicsCPVenergy productionpredictionanalysis |
| spellingShingle | Allen Barnett Xiaoting Wang Francisco J. Gómez-Gil Analysis and Prediction of Energy Production in Concentrating Photovoltaic (CPV) Installations Energies concentrating photovoltaics CPV energy production prediction analysis |
| title | Analysis and Prediction of Energy Production in Concentrating Photovoltaic (CPV) Installations |
| title_full | Analysis and Prediction of Energy Production in Concentrating Photovoltaic (CPV) Installations |
| title_fullStr | Analysis and Prediction of Energy Production in Concentrating Photovoltaic (CPV) Installations |
| title_full_unstemmed | Analysis and Prediction of Energy Production in Concentrating Photovoltaic (CPV) Installations |
| title_short | Analysis and Prediction of Energy Production in Concentrating Photovoltaic (CPV) Installations |
| title_sort | analysis and prediction of energy production in concentrating photovoltaic cpv installations |
| topic | concentrating photovoltaics CPV energy production prediction analysis |
| url | http://www.mdpi.com/1996-1073/5/3/770/ |
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