A Multi-Level World Comprehensive Neural Network Model for Maximum Annual Solar Irradiation on a Flat Surface

Ramez Abdallah, Emad Natsheh, Adel Juaidi, Sufyan Samara and Francisco Manzano-Agugliaro
Additional contact information
Ramez Abdallah: Department of Mechanical Engineering, Faculty of Engineering & Information Technology, An-Najah National University, P.O. Box 7, Nablus, Palestine
Emad Natsheh: Department of Computer Engineering, Faculty of Engineering & Information Technology, An-Najah National University, P.O. Box 7, Nablus, Palestine
Adel Juaidi: Department of Mechanical Engineering, Faculty of Engineering & Information Technology, An-Najah National University, P.O. Box 7, Nablus, Palestine
Sufyan Samara: Department of Computer Engineering, Faculty of Engineering & Information Technology, An-Najah National University, P.O. Box 7, Nablus, Palestine
Francisco Manzano-Agugliaro: Department of Engineering, University of Almeria, ceiA3, 04120 Almeria, Spain

Energies, 2020, vol. 13, issue 23, 1-31

Abstract: 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.

Keywords: optimal tilt angle; PV system; solar photovoltaic; solar irradiation; Levenberg Marquardt algorithm; feed-forward neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/23/6422/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/23/6422/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:23:p:6422-:d:457013

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().