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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ramez Abdallah & Emad Natsheh & Adel Juaidi & Sufyan Samara & Francisco Manzano-Agugliaro, 2020. "A Multi-Level World Comprehensive Neural Network Model for Maximum Annual Solar Irradiation on a Flat Surface," Energies, MDPI, vol. 13(23), pages 1-31, December.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6422-:d:457013
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    References listed on IDEAS

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    5. Youssef Kassem & Hüseyin Gökçekuş & Ali Güvensoy, 2021. "Techno-Economic Feasibility of Grid-Connected Solar PV System at Near East University Hospital, Northern Cyprus," Energies, MDPI, vol. 14(22), pages 1-27, November.
    6. Aiman Albatayneh & Adel Juaidi & Ramez Abdallah & Francisco Manzano-Agugliaro, 2021. "Influence of the Advancement in the LED Lighting Technologies on the Optimum Windows-to-Wall Ratio of Jordanians Residential Buildings," Energies, MDPI, vol. 14(17), pages 1-20, September.

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