Energy Efficiency Improvement in Photovoltaic Installation Using a Twin-Axis Solar Tracking Mechanism with LDR Sensors Compared with Neuro-Fuzzy Adaptive Inference Structure
Abstract The use of solar energy makes it possible to eliminate the problems associated with conventional energy sources such as fossil and nuclear fuels. The concerns are numerous and include the depletion of these sources and the negative environmental effects on our planet and our health. In this...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hadroug, Nadji [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Journal of electrical engineering & technology - [Singapore] : Springer Singapore, 2006, 18(2023), 4 vom: 15. Feb., Seite 2943-2967 |
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| Übergeordnetes Werk: |
volume:18 ; year:2023 ; number:4 ; day:15 ; month:02 ; pages:2943-2967 |
| Links: |
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| DOI / URN: |
10.1007/s42835-023-01411-4 |
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SPR052129500 |
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| 520 | |a Abstract The use of solar energy makes it possible to eliminate the problems associated with conventional energy sources such as fossil and nuclear fuels. The concerns are numerous and include the depletion of these sources and the negative environmental effects on our planet and our health. In this respect, this work focused on energy efficiency improvement in photovoltaic installations for sustainable development by proposing a new implementation idea applied to solar tracking mechanisms. It allows tracking in real-time the apparent movement of the sun and therefore exploits most of the irradiated solar energy. To monitor the photovoltaic systems in real-time, based on two techniques, to track the highest intensity of sunlight. Where, the first comprises configuring the twin-axis solar tracking mechanism using Light-Dependent-Resistor sensors in an easy-to-operate way, with printed circuit boards specific to this configuration. The second technique is based on the use of artificial intelligence concepts, such as neuro-fuzzy technology with an adaptive inference structure, without the use of Light-Dependent-Resistor sensors. This configuration allows the location of the sun to be accurately tracked throughout the year using positional solar track data (azimuth/elevation), ensuring improved efficiency with better performance using the neuro-fuzzy adaptive inference structure. Hence, this twin-axis solar tracking mechanism maximizes the efficiency energy of the examined photovoltaic installation by guaranteeing solar energy gains of up to 24.44% gain compared to a fixed system. | ||
| 650 | 4 | |a Photovoltaic system (PV) |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Monitoring |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Axis solar tracking mechanism |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Neuro-fuzzy approach |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a Energy production |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Energy efficiency |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Solar radiation |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Iratni, Abdelhamid |4 aut | |
| 700 | 1 | |a Hafaifa, Ahmed |0 (orcid)0000-0002-7812-7429 |4 aut | |
| 700 | 1 | |a Boudjemline, Attia |4 aut | |
| 700 | 1 | |a Alshammari, Obaid S. |4 aut | |
| 700 | 1 | |a Jerbi, Houssem |4 aut | |
| 700 | 1 | |a Colak, Ilhami |4 aut | |
| 700 | 1 | |a Chen, XiaoQi |4 aut | |
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10.1007/s42835-023-01411-4 doi (DE-627)SPR052129500 (SPR)s42835-023-01411-4-e DE-627 ger DE-627 rakwb eng Hadroug, Nadji verfasserin aut Energy Efficiency Improvement in Photovoltaic Installation Using a Twin-Axis Solar Tracking Mechanism with LDR Sensors Compared with Neuro-Fuzzy Adaptive Inference Structure 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The use of solar energy makes it possible to eliminate the problems associated with conventional energy sources such as fossil and nuclear fuels. The concerns are numerous and include the depletion of these sources and the negative environmental effects on our planet and our health. In this respect, this work focused on energy efficiency improvement in photovoltaic installations for sustainable development by proposing a new implementation idea applied to solar tracking mechanisms. It allows tracking in real-time the apparent movement of the sun and therefore exploits most of the irradiated solar energy. To monitor the photovoltaic systems in real-time, based on two techniques, to track the highest intensity of sunlight. Where, the first comprises configuring the twin-axis solar tracking mechanism using Light-Dependent-Resistor sensors in an easy-to-operate way, with printed circuit boards specific to this configuration. The second technique is based on the use of artificial intelligence concepts, such as neuro-fuzzy technology with an adaptive inference structure, without the use of Light-Dependent-Resistor sensors. This configuration allows the location of the sun to be accurately tracked throughout the year using positional solar track data (azimuth/elevation), ensuring improved efficiency with better performance using the neuro-fuzzy adaptive inference structure. Hence, this twin-axis solar tracking mechanism maximizes the efficiency energy of the examined photovoltaic installation by guaranteeing solar energy gains of up to 24.44% gain compared to a fixed system. Photovoltaic system (PV) (dpeaa)DE-He213 Monitoring (dpeaa)DE-He213 Axis solar tracking mechanism (dpeaa)DE-He213 Neuro-fuzzy approach (dpeaa)DE-He213 Adaptive inference system (dpeaa)DE-He213 Energy production (dpeaa)DE-He213 Energy efficiency (dpeaa)DE-He213 Solar radiation (dpeaa)DE-He213 Iratni, Abdelhamid aut Hafaifa, Ahmed (orcid)0000-0002-7812-7429 aut Boudjemline, Attia aut Alshammari, Obaid S. aut Jerbi, Houssem aut Colak, Ilhami aut Chen, XiaoQi aut Enthalten in Journal of electrical engineering & technology [Singapore] : Springer Singapore, 2006 18(2023), 4 vom: 15. Feb., Seite 2943-2967 (DE-627)519202015 (DE-600)2255142-6 2093-7423 nnns volume:18 year:2023 number:4 day:15 month:02 pages:2943-2967 https://dx.doi.org/10.1007/s42835-023-01411-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 4 15 02 2943-2967 |
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10.1007/s42835-023-01411-4 doi (DE-627)SPR052129500 (SPR)s42835-023-01411-4-e DE-627 ger DE-627 rakwb eng Hadroug, Nadji verfasserin aut Energy Efficiency Improvement in Photovoltaic Installation Using a Twin-Axis Solar Tracking Mechanism with LDR Sensors Compared with Neuro-Fuzzy Adaptive Inference Structure 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The use of solar energy makes it possible to eliminate the problems associated with conventional energy sources such as fossil and nuclear fuels. The concerns are numerous and include the depletion of these sources and the negative environmental effects on our planet and our health. In this respect, this work focused on energy efficiency improvement in photovoltaic installations for sustainable development by proposing a new implementation idea applied to solar tracking mechanisms. It allows tracking in real-time the apparent movement of the sun and therefore exploits most of the irradiated solar energy. To monitor the photovoltaic systems in real-time, based on two techniques, to track the highest intensity of sunlight. Where, the first comprises configuring the twin-axis solar tracking mechanism using Light-Dependent-Resistor sensors in an easy-to-operate way, with printed circuit boards specific to this configuration. The second technique is based on the use of artificial intelligence concepts, such as neuro-fuzzy technology with an adaptive inference structure, without the use of Light-Dependent-Resistor sensors. This configuration allows the location of the sun to be accurately tracked throughout the year using positional solar track data (azimuth/elevation), ensuring improved efficiency with better performance using the neuro-fuzzy adaptive inference structure. Hence, this twin-axis solar tracking mechanism maximizes the efficiency energy of the examined photovoltaic installation by guaranteeing solar energy gains of up to 24.44% gain compared to a fixed system. Photovoltaic system (PV) (dpeaa)DE-He213 Monitoring (dpeaa)DE-He213 Axis solar tracking mechanism (dpeaa)DE-He213 Neuro-fuzzy approach (dpeaa)DE-He213 Adaptive inference system (dpeaa)DE-He213 Energy production (dpeaa)DE-He213 Energy efficiency (dpeaa)DE-He213 Solar radiation (dpeaa)DE-He213 Iratni, Abdelhamid aut Hafaifa, Ahmed (orcid)0000-0002-7812-7429 aut Boudjemline, Attia aut Alshammari, Obaid S. aut Jerbi, Houssem aut Colak, Ilhami aut Chen, XiaoQi aut Enthalten in Journal of electrical engineering & technology [Singapore] : Springer Singapore, 2006 18(2023), 4 vom: 15. Feb., Seite 2943-2967 (DE-627)519202015 (DE-600)2255142-6 2093-7423 nnns volume:18 year:2023 number:4 day:15 month:02 pages:2943-2967 https://dx.doi.org/10.1007/s42835-023-01411-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 4 15 02 2943-2967 |
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10.1007/s42835-023-01411-4 doi (DE-627)SPR052129500 (SPR)s42835-023-01411-4-e DE-627 ger DE-627 rakwb eng Hadroug, Nadji verfasserin aut Energy Efficiency Improvement in Photovoltaic Installation Using a Twin-Axis Solar Tracking Mechanism with LDR Sensors Compared with Neuro-Fuzzy Adaptive Inference Structure 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The use of solar energy makes it possible to eliminate the problems associated with conventional energy sources such as fossil and nuclear fuels. The concerns are numerous and include the depletion of these sources and the negative environmental effects on our planet and our health. In this respect, this work focused on energy efficiency improvement in photovoltaic installations for sustainable development by proposing a new implementation idea applied to solar tracking mechanisms. It allows tracking in real-time the apparent movement of the sun and therefore exploits most of the irradiated solar energy. To monitor the photovoltaic systems in real-time, based on two techniques, to track the highest intensity of sunlight. Where, the first comprises configuring the twin-axis solar tracking mechanism using Light-Dependent-Resistor sensors in an easy-to-operate way, with printed circuit boards specific to this configuration. The second technique is based on the use of artificial intelligence concepts, such as neuro-fuzzy technology with an adaptive inference structure, without the use of Light-Dependent-Resistor sensors. This configuration allows the location of the sun to be accurately tracked throughout the year using positional solar track data (azimuth/elevation), ensuring improved efficiency with better performance using the neuro-fuzzy adaptive inference structure. Hence, this twin-axis solar tracking mechanism maximizes the efficiency energy of the examined photovoltaic installation by guaranteeing solar energy gains of up to 24.44% gain compared to a fixed system. Photovoltaic system (PV) (dpeaa)DE-He213 Monitoring (dpeaa)DE-He213 Axis solar tracking mechanism (dpeaa)DE-He213 Neuro-fuzzy approach (dpeaa)DE-He213 Adaptive inference system (dpeaa)DE-He213 Energy production (dpeaa)DE-He213 Energy efficiency (dpeaa)DE-He213 Solar radiation (dpeaa)DE-He213 Iratni, Abdelhamid aut Hafaifa, Ahmed (orcid)0000-0002-7812-7429 aut Boudjemline, Attia aut Alshammari, Obaid S. aut Jerbi, Houssem aut Colak, Ilhami aut Chen, XiaoQi aut Enthalten in Journal of electrical engineering & technology [Singapore] : Springer Singapore, 2006 18(2023), 4 vom: 15. Feb., Seite 2943-2967 (DE-627)519202015 (DE-600)2255142-6 2093-7423 nnns volume:18 year:2023 number:4 day:15 month:02 pages:2943-2967 https://dx.doi.org/10.1007/s42835-023-01411-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 4 15 02 2943-2967 |
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10.1007/s42835-023-01411-4 doi (DE-627)SPR052129500 (SPR)s42835-023-01411-4-e DE-627 ger DE-627 rakwb eng Hadroug, Nadji verfasserin aut Energy Efficiency Improvement in Photovoltaic Installation Using a Twin-Axis Solar Tracking Mechanism with LDR Sensors Compared with Neuro-Fuzzy Adaptive Inference Structure 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The use of solar energy makes it possible to eliminate the problems associated with conventional energy sources such as fossil and nuclear fuels. The concerns are numerous and include the depletion of these sources and the negative environmental effects on our planet and our health. In this respect, this work focused on energy efficiency improvement in photovoltaic installations for sustainable development by proposing a new implementation idea applied to solar tracking mechanisms. It allows tracking in real-time the apparent movement of the sun and therefore exploits most of the irradiated solar energy. To monitor the photovoltaic systems in real-time, based on two techniques, to track the highest intensity of sunlight. Where, the first comprises configuring the twin-axis solar tracking mechanism using Light-Dependent-Resistor sensors in an easy-to-operate way, with printed circuit boards specific to this configuration. The second technique is based on the use of artificial intelligence concepts, such as neuro-fuzzy technology with an adaptive inference structure, without the use of Light-Dependent-Resistor sensors. This configuration allows the location of the sun to be accurately tracked throughout the year using positional solar track data (azimuth/elevation), ensuring improved efficiency with better performance using the neuro-fuzzy adaptive inference structure. Hence, this twin-axis solar tracking mechanism maximizes the efficiency energy of the examined photovoltaic installation by guaranteeing solar energy gains of up to 24.44% gain compared to a fixed system. Photovoltaic system (PV) (dpeaa)DE-He213 Monitoring (dpeaa)DE-He213 Axis solar tracking mechanism (dpeaa)DE-He213 Neuro-fuzzy approach (dpeaa)DE-He213 Adaptive inference system (dpeaa)DE-He213 Energy production (dpeaa)DE-He213 Energy efficiency (dpeaa)DE-He213 Solar radiation (dpeaa)DE-He213 Iratni, Abdelhamid aut Hafaifa, Ahmed (orcid)0000-0002-7812-7429 aut Boudjemline, Attia aut Alshammari, Obaid S. aut Jerbi, Houssem aut Colak, Ilhami aut Chen, XiaoQi aut Enthalten in Journal of electrical engineering & technology [Singapore] : Springer Singapore, 2006 18(2023), 4 vom: 15. Feb., Seite 2943-2967 (DE-627)519202015 (DE-600)2255142-6 2093-7423 nnns volume:18 year:2023 number:4 day:15 month:02 pages:2943-2967 https://dx.doi.org/10.1007/s42835-023-01411-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 4 15 02 2943-2967 |
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10.1007/s42835-023-01411-4 doi (DE-627)SPR052129500 (SPR)s42835-023-01411-4-e DE-627 ger DE-627 rakwb eng Hadroug, Nadji verfasserin aut Energy Efficiency Improvement in Photovoltaic Installation Using a Twin-Axis Solar Tracking Mechanism with LDR Sensors Compared with Neuro-Fuzzy Adaptive Inference Structure 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The use of solar energy makes it possible to eliminate the problems associated with conventional energy sources such as fossil and nuclear fuels. The concerns are numerous and include the depletion of these sources and the negative environmental effects on our planet and our health. In this respect, this work focused on energy efficiency improvement in photovoltaic installations for sustainable development by proposing a new implementation idea applied to solar tracking mechanisms. It allows tracking in real-time the apparent movement of the sun and therefore exploits most of the irradiated solar energy. To monitor the photovoltaic systems in real-time, based on two techniques, to track the highest intensity of sunlight. Where, the first comprises configuring the twin-axis solar tracking mechanism using Light-Dependent-Resistor sensors in an easy-to-operate way, with printed circuit boards specific to this configuration. The second technique is based on the use of artificial intelligence concepts, such as neuro-fuzzy technology with an adaptive inference structure, without the use of Light-Dependent-Resistor sensors. This configuration allows the location of the sun to be accurately tracked throughout the year using positional solar track data (azimuth/elevation), ensuring improved efficiency with better performance using the neuro-fuzzy adaptive inference structure. Hence, this twin-axis solar tracking mechanism maximizes the efficiency energy of the examined photovoltaic installation by guaranteeing solar energy gains of up to 24.44% gain compared to a fixed system. Photovoltaic system (PV) (dpeaa)DE-He213 Monitoring (dpeaa)DE-He213 Axis solar tracking mechanism (dpeaa)DE-He213 Neuro-fuzzy approach (dpeaa)DE-He213 Adaptive inference system (dpeaa)DE-He213 Energy production (dpeaa)DE-He213 Energy efficiency (dpeaa)DE-He213 Solar radiation (dpeaa)DE-He213 Iratni, Abdelhamid aut Hafaifa, Ahmed (orcid)0000-0002-7812-7429 aut Boudjemline, Attia aut Alshammari, Obaid S. aut Jerbi, Houssem aut Colak, Ilhami aut Chen, XiaoQi aut Enthalten in Journal of electrical engineering & technology [Singapore] : Springer Singapore, 2006 18(2023), 4 vom: 15. Feb., Seite 2943-2967 (DE-627)519202015 (DE-600)2255142-6 2093-7423 nnns volume:18 year:2023 number:4 day:15 month:02 pages:2943-2967 https://dx.doi.org/10.1007/s42835-023-01411-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 4 15 02 2943-2967 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR052129500</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230703064623.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230703s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s42835-023-01411-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR052129500</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s42835-023-01411-4-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Hadroug, Nadji</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Energy Efficiency Improvement in Photovoltaic Installation Using a Twin-Axis Solar Tracking Mechanism with LDR Sensors Compared with Neuro-Fuzzy Adaptive Inference Structure</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The use of solar energy makes it possible to eliminate the problems associated with conventional energy sources such as fossil and nuclear fuels. The concerns are numerous and include the depletion of these sources and the negative environmental effects on our planet and our health. In this respect, this work focused on energy efficiency improvement in photovoltaic installations for sustainable development by proposing a new implementation idea applied to solar tracking mechanisms. It allows tracking in real-time the apparent movement of the sun and therefore exploits most of the irradiated solar energy. To monitor the photovoltaic systems in real-time, based on two techniques, to track the highest intensity of sunlight. Where, the first comprises configuring the twin-axis solar tracking mechanism using Light-Dependent-Resistor sensors in an easy-to-operate way, with printed circuit boards specific to this configuration. The second technique is based on the use of artificial intelligence concepts, such as neuro-fuzzy technology with an adaptive inference structure, without the use of Light-Dependent-Resistor sensors. This configuration allows the location of the sun to be accurately tracked throughout the year using positional solar track data (azimuth/elevation), ensuring improved efficiency with better performance using the neuro-fuzzy adaptive inference structure. Hence, this twin-axis solar tracking mechanism maximizes the efficiency energy of the examined photovoltaic installation by guaranteeing solar energy gains of up to 24.44% gain compared to a fixed system.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Photovoltaic system (PV)</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Monitoring</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Axis solar tracking mechanism</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neuro-fuzzy approach</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Adaptive inference system</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Energy production</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Energy efficiency</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Solar radiation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Iratni, Abdelhamid</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hafaifa, Ahmed</subfield><subfield code="0">(orcid)0000-0002-7812-7429</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Boudjemline, Attia</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Alshammari, Obaid S.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jerbi, Houssem</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Colak, Ilhami</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, XiaoQi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of electrical engineering & technology</subfield><subfield code="d">[Singapore] : Springer Singapore, 2006</subfield><subfield code="g">18(2023), 4 vom: 15. 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Hadroug, Nadji |
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Hadroug, Nadji misc Photovoltaic system (PV) misc Monitoring misc Axis solar tracking mechanism misc Neuro-fuzzy approach misc Adaptive inference system misc Energy production misc Energy efficiency misc Solar radiation Energy Efficiency Improvement in Photovoltaic Installation Using a Twin-Axis Solar Tracking Mechanism with LDR Sensors Compared with Neuro-Fuzzy Adaptive Inference Structure |
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Energy Efficiency Improvement in Photovoltaic Installation Using a Twin-Axis Solar Tracking Mechanism with LDR Sensors Compared with Neuro-Fuzzy Adaptive Inference Structure Photovoltaic system (PV) (dpeaa)DE-He213 Monitoring (dpeaa)DE-He213 Axis solar tracking mechanism (dpeaa)DE-He213 Neuro-fuzzy approach (dpeaa)DE-He213 Adaptive inference system (dpeaa)DE-He213 Energy production (dpeaa)DE-He213 Energy efficiency (dpeaa)DE-He213 Solar radiation (dpeaa)DE-He213 |
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10.1007/s42835-023-01411-4 |
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(orcid)0000-0002-7812-7429 |
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energy efficiency improvement in photovoltaic installation using a twin-axis solar tracking mechanism with ldr sensors compared with neuro-fuzzy adaptive inference structure |
| title_auth |
Energy Efficiency Improvement in Photovoltaic Installation Using a Twin-Axis Solar Tracking Mechanism with LDR Sensors Compared with Neuro-Fuzzy Adaptive Inference Structure |
| abstract |
Abstract The use of solar energy makes it possible to eliminate the problems associated with conventional energy sources such as fossil and nuclear fuels. The concerns are numerous and include the depletion of these sources and the negative environmental effects on our planet and our health. In this respect, this work focused on energy efficiency improvement in photovoltaic installations for sustainable development by proposing a new implementation idea applied to solar tracking mechanisms. It allows tracking in real-time the apparent movement of the sun and therefore exploits most of the irradiated solar energy. To monitor the photovoltaic systems in real-time, based on two techniques, to track the highest intensity of sunlight. Where, the first comprises configuring the twin-axis solar tracking mechanism using Light-Dependent-Resistor sensors in an easy-to-operate way, with printed circuit boards specific to this configuration. The second technique is based on the use of artificial intelligence concepts, such as neuro-fuzzy technology with an adaptive inference structure, without the use of Light-Dependent-Resistor sensors. This configuration allows the location of the sun to be accurately tracked throughout the year using positional solar track data (azimuth/elevation), ensuring improved efficiency with better performance using the neuro-fuzzy adaptive inference structure. Hence, this twin-axis solar tracking mechanism maximizes the efficiency energy of the examined photovoltaic installation by guaranteeing solar energy gains of up to 24.44% gain compared to a fixed system. © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract The use of solar energy makes it possible to eliminate the problems associated with conventional energy sources such as fossil and nuclear fuels. The concerns are numerous and include the depletion of these sources and the negative environmental effects on our planet and our health. In this respect, this work focused on energy efficiency improvement in photovoltaic installations for sustainable development by proposing a new implementation idea applied to solar tracking mechanisms. It allows tracking in real-time the apparent movement of the sun and therefore exploits most of the irradiated solar energy. To monitor the photovoltaic systems in real-time, based on two techniques, to track the highest intensity of sunlight. Where, the first comprises configuring the twin-axis solar tracking mechanism using Light-Dependent-Resistor sensors in an easy-to-operate way, with printed circuit boards specific to this configuration. The second technique is based on the use of artificial intelligence concepts, such as neuro-fuzzy technology with an adaptive inference structure, without the use of Light-Dependent-Resistor sensors. This configuration allows the location of the sun to be accurately tracked throughout the year using positional solar track data (azimuth/elevation), ensuring improved efficiency with better performance using the neuro-fuzzy adaptive inference structure. Hence, this twin-axis solar tracking mechanism maximizes the efficiency energy of the examined photovoltaic installation by guaranteeing solar energy gains of up to 24.44% gain compared to a fixed system. © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract The use of solar energy makes it possible to eliminate the problems associated with conventional energy sources such as fossil and nuclear fuels. The concerns are numerous and include the depletion of these sources and the negative environmental effects on our planet and our health. In this respect, this work focused on energy efficiency improvement in photovoltaic installations for sustainable development by proposing a new implementation idea applied to solar tracking mechanisms. It allows tracking in real-time the apparent movement of the sun and therefore exploits most of the irradiated solar energy. To monitor the photovoltaic systems in real-time, based on two techniques, to track the highest intensity of sunlight. Where, the first comprises configuring the twin-axis solar tracking mechanism using Light-Dependent-Resistor sensors in an easy-to-operate way, with printed circuit boards specific to this configuration. The second technique is based on the use of artificial intelligence concepts, such as neuro-fuzzy technology with an adaptive inference structure, without the use of Light-Dependent-Resistor sensors. This configuration allows the location of the sun to be accurately tracked throughout the year using positional solar track data (azimuth/elevation), ensuring improved efficiency with better performance using the neuro-fuzzy adaptive inference structure. Hence, this twin-axis solar tracking mechanism maximizes the efficiency energy of the examined photovoltaic installation by guaranteeing solar energy gains of up to 24.44% gain compared to a fixed system. © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Energy Efficiency Improvement in Photovoltaic Installation Using a Twin-Axis Solar Tracking Mechanism with LDR Sensors Compared with Neuro-Fuzzy Adaptive Inference Structure |
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Iratni, Abdelhamid Hafaifa, Ahmed Boudjemline, Attia Alshammari, Obaid S. Jerbi, Houssem Colak, Ilhami Chen, XiaoQi |
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Iratni, Abdelhamid Hafaifa, Ahmed Boudjemline, Attia Alshammari, Obaid S. Jerbi, Houssem Colak, Ilhami Chen, XiaoQi |
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| score |
7.400483 |