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Multiple-Regression Method for Fast Estimation of Solar Irradiation and Photovoltaic Energy Potentials over Europe and Africa

Author

Listed:
  • Alberto Bocca

    (Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy)

  • Luca Bergamasco

    (Department of Energy, Politecnico di Torino, 10129 Turin, Italy)

  • Matteo Fasano

    (Department of Energy, Politecnico di Torino, 10129 Turin, Italy)

  • Lorenzo Bottaccioli

    (Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy)

  • Eliodoro Chiavazzo

    (Department of Energy, Politecnico di Torino, 10129 Turin, Italy)

  • Alberto Macii

    (Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy)

  • Pietro Asinari

    (Department of Energy, Politecnico di Torino, 10129 Turin, Italy)

Abstract

In recent years, various online tools and databases have been developed to assess the potential energy output of photovoltaic (PV) installations in different geographical areas. However, these tools generally provide a spatial resolution of a few kilometers and, for a systematic analysis at large scale, they require continuous querying of their online databases. In this article, we present a methodology for fast estimation of the yearly sum of global solar irradiation and PV energy yield over large-scale territories. The proposed method relies on a multiple-regression model including only well-known geodata, such as latitude, altitude above sea level and average ambient temperature. Therefore, it is particularly suitable for a fast, preliminary, offline estimation of solar PV output and to analyze possible investments in new installations. Application of the method to a random set of 80 geographical locations throughout Europe and Africa yields a mean absolute percent error of 4.4% for the estimate of solar irradiation (13.6% maximum percent error) and of 4.3% for the prediction of photovoltaic electricity production (14.8% maximum percent error for free-standing installations; 15.4% for building-integrated ones), which are consistent with the general accuracy provided by the reference tools for this application. Besides photovoltaic potentials, the proposed method could also find application in a wider range of installation assessments, such as in solar thermal energy or desalination plants.

Suggested Citation

  • Alberto Bocca & Luca Bergamasco & Matteo Fasano & Lorenzo Bottaccioli & Eliodoro Chiavazzo & Alberto Macii & Pietro Asinari, 2018. "Multiple-Regression Method for Fast Estimation of Solar Irradiation and Photovoltaic Energy Potentials over Europe and Africa," Energies, MDPI, vol. 11(12), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3477-:d:190249
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    References listed on IDEAS

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    5. Maria. C. Bueso & José Miguel Paredes-Parra & Antonio Mateo-Aroca & Angel Molina-García, 2020. "A Characterization of Metrics for Comparing Satellite-Based and Ground-Measured Global Horizontal Irradiance Data: A Principal Component Analysis Application," Sustainability, MDPI, vol. 12(6), pages 1-18, March.
    6. Olubayo M. Babatunde & Josiah L. Munda & Yskandar Hamam, 2020. "Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation," Energies, MDPI, vol. 13(10), pages 1-18, May.
    7. Alba Vilanova & Bo-Young Kim & Chang Ki Kim & Hyun-Goo Kim, 2020. "Linear-Gompertz Model-Based Regression of Photovoltaic Power Generation by Satellite Imagery-Based Solar Irradiance," Energies, MDPI, vol. 13(4), pages 1-12, February.
    8. Qi Wang & Ping Chang & Runqing Bai & Wenfei Liu & Jianfeng Dai & Yi Tang, 2019. "Mitigation Strategy for Duck Curve in High Photovoltaic Penetration Power System Using Concentrating Solar Power Station," Energies, MDPI, vol. 12(18), pages 1-16, September.

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