Towards the intrahour forecasting of direct normal irradiance using sky-imaging data
Increasing power plant efficiency through improved operation is key in the development of Concentrating Solar Power (CSP) technologies. To this end, one of the most challenging topics remains accurately forecasting the solar resource at a short-term horizon. Indeed, in CSP plants, production is dire...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2018-04-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844017320893 |
| _version_ | 1819182680220106752 |
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| author | Julien Nou Rémi Chauvin Julien Eynard Stéphane Thil Stéphane Grieu |
| author_facet | Julien Nou Rémi Chauvin Julien Eynard Stéphane Thil Stéphane Grieu |
| author_sort | Julien Nou |
| collection | DOAJ |
| description | Increasing power plant efficiency through improved operation is key in the development of Concentrating Solar Power (CSP) technologies. To this end, one of the most challenging topics remains accurately forecasting the solar resource at a short-term horizon. Indeed, in CSP plants, production is directly impacted by both the availability and variability of the solar resource and, more specifically, by Direct Normal Irradiance (DNI). The present paper deals with a new approach to the intrahour forecasting (the forecast horizon Δtf is up to 30min ahead) of DNI, taking advantage of the fact that this quantity can be split into two terms, i.e. clear-sky DNI and the clear sky index. Clear-sky DNI is forecasted from DNI measurements, using an empirical model (Ineichen and Perez, 2002) combined with a persistence of atmospheric turbidity. Moreover, in the framework of the CSPIMP (Concentrating Solar Power plant efficiency IMProvement) research project, PROMES-CNRS has developed a sky imager able to provide High Dynamic Range (HDR) images. So, regarding the clear-sky index, it is forecasted from sky-imaging data, using an Adaptive Network-based Fuzzy Inference System (ANFIS). A hybrid algorithm that takes inspiration from the classification algorithm proposed by Ghonima et al. (2012) when clear-sky anisotropy is known and from the hybrid thresholding algorithm proposed by Li et al. (2011) in the opposite case has been developed to the detection of clouds. Performance is evaluated via a comparative study in which persistence models – either a persistence of DNI or a persistence of the clear-sky index – are included. Preliminary results highlight that the proposed approach has the potential to outperform these models (both persistence models achieve similar performance) in terms of forecasting accuracy: over the test data used, RMSE (the Root Mean Square Error) is reduced of about 20Wm−2, with Δtf=15min, and 40Wm−2, with Δtf=30min. |
| first_indexed | 2024-12-22T22:49:58Z |
| format | Article |
| id | doaj.art-c73cd4515581481389c93e9fc9a247d0 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-12-22T22:49:58Z |
| publishDate | 2018-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-c73cd4515581481389c93e9fc9a247d02022-12-21T18:09:59ZengElsevierHeliyon2405-84402018-04-0144e00598Towards the intrahour forecasting of direct normal irradiance using sky-imaging dataJulien Nou0Rémi Chauvin1Julien Eynard2Stéphane Thil3Stéphane Grieu4PROMES-CNRS (UPR 8521), Rambla de la thermodynamique, Tecnosud, 66100 Perpignan, FrancePROMES-CNRS (UPR 8521), Rambla de la thermodynamique, Tecnosud, 66100 Perpignan, FrancePROMES-CNRS (UPR 8521), Rambla de la thermodynamique, Tecnosud, 66100 Perpignan, France; Université de Perpignan Via Domitia, 52 Avenue Paul Alduy, 66860 Perpignan, FrancePROMES-CNRS (UPR 8521), Rambla de la thermodynamique, Tecnosud, 66100 Perpignan, France; Université de Perpignan Via Domitia, 52 Avenue Paul Alduy, 66860 Perpignan, FrancePROMES-CNRS (UPR 8521), Rambla de la thermodynamique, Tecnosud, 66100 Perpignan, France; Université de Perpignan Via Domitia, 52 Avenue Paul Alduy, 66860 Perpignan, France; Corresponding author.Increasing power plant efficiency through improved operation is key in the development of Concentrating Solar Power (CSP) technologies. To this end, one of the most challenging topics remains accurately forecasting the solar resource at a short-term horizon. Indeed, in CSP plants, production is directly impacted by both the availability and variability of the solar resource and, more specifically, by Direct Normal Irradiance (DNI). The present paper deals with a new approach to the intrahour forecasting (the forecast horizon Δtf is up to 30min ahead) of DNI, taking advantage of the fact that this quantity can be split into two terms, i.e. clear-sky DNI and the clear sky index. Clear-sky DNI is forecasted from DNI measurements, using an empirical model (Ineichen and Perez, 2002) combined with a persistence of atmospheric turbidity. Moreover, in the framework of the CSPIMP (Concentrating Solar Power plant efficiency IMProvement) research project, PROMES-CNRS has developed a sky imager able to provide High Dynamic Range (HDR) images. So, regarding the clear-sky index, it is forecasted from sky-imaging data, using an Adaptive Network-based Fuzzy Inference System (ANFIS). A hybrid algorithm that takes inspiration from the classification algorithm proposed by Ghonima et al. (2012) when clear-sky anisotropy is known and from the hybrid thresholding algorithm proposed by Li et al. (2011) in the opposite case has been developed to the detection of clouds. Performance is evaluated via a comparative study in which persistence models – either a persistence of DNI or a persistence of the clear-sky index – are included. Preliminary results highlight that the proposed approach has the potential to outperform these models (both persistence models achieve similar performance) in terms of forecasting accuracy: over the test data used, RMSE (the Root Mean Square Error) is reduced of about 20Wm−2, with Δtf=15min, and 40Wm−2, with Δtf=30min.http://www.sciencedirect.com/science/article/pii/S2405844017320893EnergyEnvironmental science |
| spellingShingle | Julien Nou Rémi Chauvin Julien Eynard Stéphane Thil Stéphane Grieu Towards the intrahour forecasting of direct normal irradiance using sky-imaging data Heliyon Energy Environmental science |
| title | Towards the intrahour forecasting of direct normal irradiance using sky-imaging data |
| title_full | Towards the intrahour forecasting of direct normal irradiance using sky-imaging data |
| title_fullStr | Towards the intrahour forecasting of direct normal irradiance using sky-imaging data |
| title_full_unstemmed | Towards the intrahour forecasting of direct normal irradiance using sky-imaging data |
| title_short | Towards the intrahour forecasting of direct normal irradiance using sky-imaging data |
| title_sort | towards the intrahour forecasting of direct normal irradiance using sky imaging data |
| topic | Energy Environmental science |
| url | http://www.sciencedirect.com/science/article/pii/S2405844017320893 |
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