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

Alberto Bocca, Luca Bergamasco, Matteo Fasano, Lorenzo Bottaccioli, Eliodoro Chiavazzo, Alberto Macii and Pietro Asinari
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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

Energies, 2018, vol. 11, issue 12, 1-17

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.

Keywords: solar energy; photovoltaic potential; renewable energy; fast energy analysis; sustainable development (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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