IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i12p3477-d190249.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/12/3477/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/12/3477/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Numbi, B.P. & Malinga, S.J., 2017. "Optimal energy cost and economic analysis of a residential grid-interactive solar PV system- case of eThekwini municipality in South Africa," Applied Energy, Elsevier, vol. 186(P1), pages 28-45.
    2. Mohan, Gowtham & Kumar, Uday & Pokhrel, Manoj Kumar & Martin, Andrew, 2016. "A novel solar thermal polygeneration system for sustainable production of cooling, clean water and domestic hot water in United Arab Emirates: Dynamic simulation and economic evaluation," Applied Energy, Elsevier, vol. 167(C), pages 173-188.
    3. Bocca, Alberto & Bottaccioli, Lorenzo & Chiavazzo, Eliodoro & Fasano, Matteo & Macii, Alberto & Asinari, Pietro, 2016. "Estimating photovoltaic energy potential from a minimal set of randomly sampled data," Renewable Energy, Elsevier, vol. 97(C), pages 457-467.
    4. Wong, L. T. & Chow, W. K., 2001. "Solar radiation model," Applied Energy, Elsevier, vol. 69(3), pages 191-224, July.
    5. Ping-Huan Kuo & Hsin-Chuan Chen & Chiou-Jye Huang, 2018. "Solar Radiation Estimation Algorithm and Field Verification in Taiwan," Energies, MDPI, vol. 11(6), pages 1-12, May.
    6. Pfenninger, Stefan, 2017. "Dealing with multiple decades of hourly wind and PV time series in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability," Applied Energy, Elsevier, vol. 197(C), pages 1-13.
    7. Thomas Huld & Ana M. Gracia Amillo, 2015. "Estimating PV Module Performance over Large Geographical Regions: The Role of Irradiance, Air Temperature, Wind Speed and Solar Spectrum," Energies, MDPI, vol. 8(6), pages 1-23, June.
    8. Martínez-Rubio, A. & Sanz-Adan, F. & Santamaría-Peña, J. & Martínez, Araceli, 2016. "Evaluating solar irradiance over facades in high building cities, based on LiDAR technology," Applied Energy, Elsevier, vol. 183(C), pages 133-147.
    9. Bocca, Alberto & Chiavazzo, Eliodoro & Macii, Alberto & Asinari, Pietro, 2015. "Solar energy potential assessment: An overview and a fast modeling approach with application to Italy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 291-296.
    10. Marucci, Alvaro & Cappuccini, Andrea, 2016. "Dynamic photovoltaic greenhouse: Energy efficiency in clear sky conditions," Applied Energy, Elsevier, vol. 170(C), pages 362-376.
    11. Rumbayan, Meita & Abudureyimu, Asifujiang & Nagasaka, Ken, 2012. "Mapping of solar energy potential in Indonesia using artificial neural network and geographical information system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(3), pages 1437-1449.
    12. Besharat, Fariba & Dehghan, Ali A. & Faghih, Ahmad R., 2013. "Empirical models for estimating global solar radiation: A review and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 798-821.
    13. Zhang, Jianyuan & Zhao, Li & Deng, Shuai & Xu, Weicong & Zhang, Ying, 2017. "A critical review of the models used to estimate solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 314-329.
    14. Andrea Micangeli & Riccardo Del Citto & Isaac Nzue Kiva & Simone Giovanni Santori & Valeria Gambino & Jeremiah Kiplagat & Daniele Viganò & Davide Fioriti & Davide Poli, 2017. "Energy Production Analysis and Optimization of Mini-Grid in Remote Areas: The Case Study of Habaswein, Kenya," Energies, MDPI, vol. 10(12), pages 1-23, December.
    15. Banjo A. Aderemi & S. P. Daniel Chowdhury & Thomas O. Olwal & Adnan M. Abu-Mahfouz, 2018. "Techno-Economic Feasibility of Hybrid Solar Photovoltaic and Battery Energy Storage Power System for a Mobile Cellular Base Station in Soshanguve, South Africa," Energies, MDPI, vol. 11(6), pages 1-26, June.
    16. Noorian, Ali Mohammad & Moradi, Isaac & Kamali, Gholam Ali, 2008. "Evaluation of 12 models to estimate hourly diffuse irradiation on inclined surfaces," Renewable Energy, Elsevier, vol. 33(6), pages 1406-1412.
    17. Henrik Zsiborács & Nóra Hegedűsné Baranyai & András Vincze & István Háber & Gábor Pintér, 2018. "Economic and Technical Aspects of Flexible Storage Photovoltaic Systems in Europe," Energies, MDPI, vol. 11(6), pages 1-17, June.
    18. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
    19. Rose, Amy & Stoner, Robert & Pérez-Arriaga, Ignacio, 2016. "Prospects for grid-connected solar PV in Kenya: A systems approach," Applied Energy, Elsevier, vol. 161(C), pages 583-590.
    20. Jabar H. Yousif & Hussein A. Kazem & John Boland, 2017. "Predictive Models for Photovoltaic Electricity Production in Hot Weather Conditions," Energies, MDPI, vol. 10(7), pages 1-19, July.
    21. Gábor Pintér & Nóra Hegedűsné Baranyai & Alec Wiliams & Henrik Zsiborács, 2018. "Study of Photovoltaics and LED Energy Efficiency: Case Study in Hungary," Energies, MDPI, vol. 11(4), pages 1-13, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Vrînceanu, Alexandra & Dumitrașcu, Monica & Kucsicsa, Gheorghe, 2022. "Site suitability for photovoltaic farms and current investment in Romania," Renewable Energy, Elsevier, vol. 187(C), pages 320-330.
    2. Carlos Toledo & Ana Maria Gracia Amillo & Giorgio Bardizza & Jose Abad & Antonio Urbina, 2020. "Evaluation of Solar Radiation Transposition Models for Passive Energy Management and Building Integrated Photovoltaics," Energies, MDPI, vol. 13(3), pages 1-24, February.
    3. De Angelis, Paolo & Tuninetti, Marta & Bergamasco, Luca & Calianno, Luca & Asinari, Pietro & Laio, Francesco & Fasano, Matteo, 2021. "Data-driven appraisal of renewable energy potentials for sustainable freshwater production in Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    4. Mohamed Derbeli & Oscar Barambones & Jose Antonio Ramos-Hernanz & Lassaad Sbita, 2019. "Real-Time Implementation of a Super Twisting Algorithm for PEM Fuel Cell Power System," Energies, MDPI, vol. 12(9), pages 1-20, April.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Khalil, Samy A. & Shaffie, A.M., 2016. "Evaluation of transposition models of solar irradiance over Egypt," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 105-119.
    2. Mashood Nasir & Hassan Abbas Khan & Irfan Khan & Naveed ul Hassan & Nauman Ahmad Zaffar & Aneeq Mehmood & Thilo Sauter & S. M. Muyeen, 2019. "Grid Load Reduction through Optimized PV Power Utilization in Intermittent Grids Using a Low-Cost Hardware Platform," Energies, MDPI, vol. 12(9), pages 1-21, May.
    3. Frank, Christopher & Fiedler, Stephanie & Crewell, Susanne, 2021. "Balancing potential of natural variability and extremes in photovoltaic and wind energy production for European countries," Renewable Energy, Elsevier, vol. 163(C), pages 674-684.
    4. Luis Arribas & Yolanda Lechón & Alberto Perula & Javier Domínguez & Manuel Ferres & Jorge Navarro & Luis F. Zarzalejo & Carolina García Barquero & Ignacio Cruz, 2021. "Review of Data and Data Sources for the Assessment of the Potential of Utility-Scale Hybrid Wind–Solar PV Power Plants Deployment, under a Microgrid Scope," Energies, MDPI, vol. 14(21), pages 1-23, November.
    5. Shirizadeh, Behrang & Quirion, Philippe, 2022. "The importance of renewable gas in achieving carbon-neutrality: Insights from an energy system optimization model," Energy, Elsevier, vol. 255(C).
    6. Chang, Kai & Zhang, Qingyuan, 2019. "Improvement of the hourly global solar model and solar radiation for air-conditioning design in China," Renewable Energy, Elsevier, vol. 138(C), pages 1232-1238.
    7. Keshtegar, Behrooz & Mert, Cihan & Kisi, Ozgur, 2018. "Comparison of four heuristic regression techniques in solar radiation modeling: Kriging method vs RSM, MARS and M5 model tree," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 330-341.
    8. 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.
    9. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    10. Xiaoyang Song & Yaohuan Huang & Chuanpeng Zhao & Yuxin Liu & Yanguo Lu & Yongguo Chang & Jie Yang, 2018. "An Approach for Estimating Solar Photovoltaic Potential Based on Rooftop Retrieval from Remote Sensing Images," Energies, MDPI, vol. 11(11), pages 1-14, November.
    11. Despotovic, Milan & Nedic, Vladimir & Despotovic, Danijela & Cvetanovic, Slobodan, 2016. "Evaluation of empirical models for predicting monthly mean horizontal diffuse solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 246-260.
    12. de Guibert, Paul & Shirizadeh, Behrang & Quirion, Philippe, 2020. "Variable time-step: A method for improving computational tractability for energy system models with long-term storage," Energy, Elsevier, vol. 213(C).
    13. Qiu, Rangjian & Li, Longan & Wu, Lifeng & Agathokleous, Evgenios & Liu, Chunwei & Zhang, Baozhong & Luo, Yufeng & Sun, Shanlei, 2022. "Modeling daily global solar radiation using only temperature data: Past, development, and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    14. Pickering, Bryn & Choudhary, Ruchi, 2021. "Quantifying resilience in energy systems with out-of-sample testing," Applied Energy, Elsevier, vol. 285(C).
    15. Maria Simona Răboacă & Gheorghe Badea & Adrian Enache & Constantin Filote & Gabriel Răsoi & Mihai Rata & Alexandru Lavric & Raluca-Andreea Felseghi, 2019. "Concentrating Solar Power Technologies," Energies, MDPI, vol. 12(6), pages 1-17, March.
    16. Lombardi, Francesco & Rocco, Matteo Vincenzo & Colombo, Emanuela, 2019. "A multi-layer energy modelling methodology to assess the impact of heat-electricity integration strategies: The case of the residential cooking sector in Italy," Energy, Elsevier, vol. 170(C), pages 1249-1260.
    17. Rivero, M. & Orozco, S. & Sellschopp, F.S. & Loera-Palomo, R., 2017. "A new methodology to extend the validity of the Hargreaves-Samani model to estimate global solar radiation in different climates: Case study Mexico," Renewable Energy, Elsevier, vol. 114(PB), pages 1340-1352.
    18. Robert Bauer & Dominik Schopf & Grégoire Klaus & Raimund Brotsack & Javier Valdes, 2022. "Energy Cell Simulation for Sector Coupling with Power-to-Methane: A Case Study in Lower Bavaria," Energies, MDPI, vol. 15(7), pages 1-22, April.
    19. Pei Huang & Xingxing Zhang & Benedetta Copertaro & Puneet Kumar Saini & Da Yan & Yi Wu & Xiangjie Chen, 2020. "A Technical Review of Modeling Techniques for Urban Solar Mobility: Solar to Buildings, Vehicles, and Storage (S2BVS)," Sustainability, MDPI, vol. 12(17), pages 1-37, August.
    20. Müller, Inga M., 2022. "Energy system modeling with aggregated time series: A profiling approach," Applied Energy, Elsevier, vol. 322(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3477-:d:190249. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.