A Multi-Level World Comprehensive Neural Network Model for Maximum Annual Solar Irradiation on a Flat Surface
With the growing demand for clean and economically feasible renewable energy, solar photovoltaic (PV) system usage has increased. Among many factors, the tilt and azimuth angles are of great importance and influence in determining the photovoltaic panel’s efficiency to generate electricity. Although...
| Main Authors: | , , , , |
|---|---|
| Format: | info:eu-repo/semantics/article |
| Sprog: | English |
| Udgivet: |
MDPI
2020
|
| Fag: | |
| Online adgang: | http://hdl.handle.net/10835/9040 |
| _version_ | 1789406352668360704 |
|---|---|
| author | Abdallah, Ramez Natsheh, Emad Juaidi, Adel Samara, Sufyan Manzano Agugliaro, Francisco |
| author_facet | Abdallah, Ramez Natsheh, Emad Juaidi, Adel Samara, Sufyan Manzano Agugliaro, Francisco |
| author_sort | Abdallah, Ramez |
| collection | DSpace |
| description | With the growing demand for clean and economically feasible renewable energy, solar photovoltaic (PV) system usage has increased. Among many factors, the tilt and azimuth angles are of great importance and influence in determining the photovoltaic panel’s efficiency to generate electricity. Although much research was conducted related to solar PV panels’ performance, this work critically determined the tilt and azimuth angles for PV panels in all countries worldwide. The optimum tilt and azimuth angles are estimated worldwide by the photovoltaic geographic information system (PVGIS). Also, annual and average daily solar irradiation incident on the tilted and oriented plate optimally (AR1 and DR1) are calculated. Besides, annual and average daily solar irradiation incident on plate tilt optimally and oriented because of the south in the northern hemisphere and because of the north in the southern hemisphere (AR2 and DR2) are estimated. PVGIS is also used to calculate the annual and average daily solar irradiation incident on the horizontal plate (AR3 and DR3). The data collected from PVGIS are used to develop an efficient and accurate artificial neural network model based on feed-forward neural network approach. This model is an essential subpart that can be used in an embedded system or an online system for further PV system analysis and optimization. The developed neural model reflected very high accuracy in predicting the PV panels’ optimal tilt and azimuth angles worldwide. The benefit of tilting is generally increased by increasing the latitude. As the latitude increases, the tilt factor (F) increases because of the increase in the optimum tilt angle by increasing the latitude. The optimal orientation is due to the north in the southern hemisphere and due to the south in the northern hemisphere for most cities worldwide. In sum, it can be concluded that the optimum tilt angle is equal to or greater than the latitude until the latitude 30°. The optimum tilt angle becomes less than the latitude, and the difference is increased until it reaches more than 20°. Hence in this study the aim is to develop a simple neural network model which can accurately predict the annual radiation and optimum tilt and azimuth angle in any region of the world and can be easily implemented in a low-cost microcontroller. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-9040 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2020 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-90402023-04-12T19:28:55Z A Multi-Level World Comprehensive Neural Network Model for Maximum Annual Solar Irradiation on a Flat Surface Abdallah, Ramez Natsheh, Emad Juaidi, Adel Samara, Sufyan Manzano Agugliaro, Francisco optimal tilt angle PV system solar photovoltaic solar irradiation Levenberg Marquardt algorithm feed-forward neural network With the growing demand for clean and economically feasible renewable energy, solar photovoltaic (PV) system usage has increased. Among many factors, the tilt and azimuth angles are of great importance and influence in determining the photovoltaic panel’s efficiency to generate electricity. Although much research was conducted related to solar PV panels’ performance, this work critically determined the tilt and azimuth angles for PV panels in all countries worldwide. The optimum tilt and azimuth angles are estimated worldwide by the photovoltaic geographic information system (PVGIS). Also, annual and average daily solar irradiation incident on the tilted and oriented plate optimally (AR1 and DR1) are calculated. Besides, annual and average daily solar irradiation incident on plate tilt optimally and oriented because of the south in the northern hemisphere and because of the north in the southern hemisphere (AR2 and DR2) are estimated. PVGIS is also used to calculate the annual and average daily solar irradiation incident on the horizontal plate (AR3 and DR3). The data collected from PVGIS are used to develop an efficient and accurate artificial neural network model based on feed-forward neural network approach. This model is an essential subpart that can be used in an embedded system or an online system for further PV system analysis and optimization. The developed neural model reflected very high accuracy in predicting the PV panels’ optimal tilt and azimuth angles worldwide. The benefit of tilting is generally increased by increasing the latitude. As the latitude increases, the tilt factor (F) increases because of the increase in the optimum tilt angle by increasing the latitude. The optimal orientation is due to the north in the southern hemisphere and due to the south in the northern hemisphere for most cities worldwide. In sum, it can be concluded that the optimum tilt angle is equal to or greater than the latitude until the latitude 30°. The optimum tilt angle becomes less than the latitude, and the difference is increased until it reaches more than 20°. Hence in this study the aim is to develop a simple neural network model which can accurately predict the annual radiation and optimum tilt and azimuth angle in any region of the world and can be easily implemented in a low-cost microcontroller. 2020-12-09T10:38:17Z 2020-12-09T10:38:17Z 2020-12-04 info:eu-repo/semantics/article 1996-1073 http://hdl.handle.net/10835/9040 en https://www.mdpi.com/1996-1073/13/23/6422 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | optimal tilt angle PV system solar photovoltaic solar irradiation Levenberg Marquardt algorithm feed-forward neural network Abdallah, Ramez Natsheh, Emad Juaidi, Adel Samara, Sufyan Manzano Agugliaro, Francisco A Multi-Level World Comprehensive Neural Network Model for Maximum Annual Solar Irradiation on a Flat Surface |
| title | A Multi-Level World Comprehensive Neural Network Model for Maximum Annual Solar Irradiation on a Flat Surface |
| title_full | A Multi-Level World Comprehensive Neural Network Model for Maximum Annual Solar Irradiation on a Flat Surface |
| title_fullStr | A Multi-Level World Comprehensive Neural Network Model for Maximum Annual Solar Irradiation on a Flat Surface |
| title_full_unstemmed | A Multi-Level World Comprehensive Neural Network Model for Maximum Annual Solar Irradiation on a Flat Surface |
| title_short | A Multi-Level World Comprehensive Neural Network Model for Maximum Annual Solar Irradiation on a Flat Surface |
| title_sort | multi-level world comprehensive neural network model for maximum annual solar irradiation on a flat surface |
| topic | optimal tilt angle PV system solar photovoltaic solar irradiation Levenberg Marquardt algorithm feed-forward neural network |
| url | http://hdl.handle.net/10835/9040 |
| work_keys_str_mv | AT abdallahramez amultilevelworldcomprehensiveneuralnetworkmodelformaximumannualsolarirradiationonaflatsurface AT natshehemad amultilevelworldcomprehensiveneuralnetworkmodelformaximumannualsolarirradiationonaflatsurface AT juaidiadel amultilevelworldcomprehensiveneuralnetworkmodelformaximumannualsolarirradiationonaflatsurface AT samarasufyan amultilevelworldcomprehensiveneuralnetworkmodelformaximumannualsolarirradiationonaflatsurface AT manzanoagugliarofrancisco amultilevelworldcomprehensiveneuralnetworkmodelformaximumannualsolarirradiationonaflatsurface AT abdallahramez multilevelworldcomprehensiveneuralnetworkmodelformaximumannualsolarirradiationonaflatsurface AT natshehemad multilevelworldcomprehensiveneuralnetworkmodelformaximumannualsolarirradiationonaflatsurface AT juaidiadel multilevelworldcomprehensiveneuralnetworkmodelformaximumannualsolarirradiationonaflatsurface AT samarasufyan multilevelworldcomprehensiveneuralnetworkmodelformaximumannualsolarirradiationonaflatsurface AT manzanoagugliarofrancisco multilevelworldcomprehensiveneuralnetworkmodelformaximumannualsolarirradiationonaflatsurface |