Optimum Solar Panel Orientation and Performance: A Climatic Data-Driven Metaheuristic Approach
This study presents an optimization platform based on the climatic data provided by the National Renewable Energy Laboratory (NREL) to determine the optimum solar panel orientation. Our optimization model is simpler to use than the clearness index model since there is no need to calculate the extrat...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/2/624 |
| _version_ | 1827665775186411520 |
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| author | Mohammad H. Naraghi Ehsan Atefi |
| author_facet | Mohammad H. Naraghi Ehsan Atefi |
| author_sort | Mohammad H. Naraghi |
| collection | DOAJ |
| description | This study presents an optimization platform based on the climatic data provided by the National Renewable Energy Laboratory (NREL) to determine the optimum solar panel orientation. Our optimization model is simpler to use than the clearness index model since there is no need to calculate the extraterrestrial insolation on a horizontal flat plate and the shape factor. This optimization approach is based on the hourly climatic data. It determines the optimum tilt angle and azimuth angle of a solar panel for the maximum power generation, considering the diurnal variation of climatic conditions. The hourly evaluation of insolation allows setting up a solar panel azimuth angle that responds to the peak power demand. The main data that impacts the solar panel performance consists of the solar direct normal incident (DNI), direct horizontal incident (DHI), global horizontal incident (GHI), ambient temperature, wind speed, and ground albedo, all of which were obtained from the NREL database for over twenty years. The accuracy of the optimization platform introduced in this study is scrutinized by investigating the three locations in the United States with different climatic conditions. The results based on the present optimization model show higher PV power than the general rule of thumb for south-facing panels with title angles the same as the latitude of the location. Moreover, the effect of deviations from optimum panel orientation is discussed to show the versatility of our technique. Our optimization model is easy-to-use, computationally efficient, and capable of being applied to other locations worldwide. |
| first_indexed | 2024-03-10T01:32:49Z |
| format | Article |
| id | doaj.art-a5b9b16c3c814117a621efc0b4e50ba7 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T01:32:49Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-a5b9b16c3c814117a621efc0b4e50ba72023-11-23T13:39:25ZengMDPI AGEnergies1996-10732022-01-0115262410.3390/en15020624Optimum Solar Panel Orientation and Performance: A Climatic Data-Driven Metaheuristic ApproachMohammad H. Naraghi0Ehsan Atefi1Department of Mechanical Engineering, Manhattan College, Riverdale, NY 10471, USADepartment of Mechanical Engineering, Manhattan College, Riverdale, NY 10471, USAThis study presents an optimization platform based on the climatic data provided by the National Renewable Energy Laboratory (NREL) to determine the optimum solar panel orientation. Our optimization model is simpler to use than the clearness index model since there is no need to calculate the extraterrestrial insolation on a horizontal flat plate and the shape factor. This optimization approach is based on the hourly climatic data. It determines the optimum tilt angle and azimuth angle of a solar panel for the maximum power generation, considering the diurnal variation of climatic conditions. The hourly evaluation of insolation allows setting up a solar panel azimuth angle that responds to the peak power demand. The main data that impacts the solar panel performance consists of the solar direct normal incident (DNI), direct horizontal incident (DHI), global horizontal incident (GHI), ambient temperature, wind speed, and ground albedo, all of which were obtained from the NREL database for over twenty years. The accuracy of the optimization platform introduced in this study is scrutinized by investigating the three locations in the United States with different climatic conditions. The results based on the present optimization model show higher PV power than the general rule of thumb for south-facing panels with title angles the same as the latitude of the location. Moreover, the effect of deviations from optimum panel orientation is discussed to show the versatility of our technique. Our optimization model is easy-to-use, computationally efficient, and capable of being applied to other locations worldwide.https://www.mdpi.com/1996-1073/15/2/624solar panel optimum orientationdata-driven solar panel insolationmetaheuristic optimizationoptimum seasonal panel orientation |
| spellingShingle | Mohammad H. Naraghi Ehsan Atefi Optimum Solar Panel Orientation and Performance: A Climatic Data-Driven Metaheuristic Approach Energies solar panel optimum orientation data-driven solar panel insolation metaheuristic optimization optimum seasonal panel orientation |
| title | Optimum Solar Panel Orientation and Performance: A Climatic Data-Driven Metaheuristic Approach |
| title_full | Optimum Solar Panel Orientation and Performance: A Climatic Data-Driven Metaheuristic Approach |
| title_fullStr | Optimum Solar Panel Orientation and Performance: A Climatic Data-Driven Metaheuristic Approach |
| title_full_unstemmed | Optimum Solar Panel Orientation and Performance: A Climatic Data-Driven Metaheuristic Approach |
| title_short | Optimum Solar Panel Orientation and Performance: A Climatic Data-Driven Metaheuristic Approach |
| title_sort | optimum solar panel orientation and performance a climatic data driven metaheuristic approach |
| topic | solar panel optimum orientation data-driven solar panel insolation metaheuristic optimization optimum seasonal panel orientation |
| url | https://www.mdpi.com/1996-1073/15/2/624 |
| work_keys_str_mv | AT mohammadhnaraghi optimumsolarpanelorientationandperformanceaclimaticdatadrivenmetaheuristicapproach AT ehsanatefi optimumsolarpanelorientationandperformanceaclimaticdatadrivenmetaheuristicapproach |