IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i16p3158-d258309.html
   My bibliography  Save this article

Evaluation of Contribution of PV Array and Inverter Configurations to Rooftop PV System Energy Yield Using Machine Learning Techniques

Author

Listed:
  • Ngoc Thien Le

    (Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand
    Department of Urban Engineering, University of Architecture Ho Chi Minh City, Ho Chi Minh City 72407, Vietnam)

  • Watit Benjapolakul

    (Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand)

Abstract

Rooftop photovoltaics (PV) systems are attracting residential customers due to their renewable energy contribution to houses and to green cities. However, customers also need a comprehensive understanding of system design configuration and the related energy return from the system in order to support their PV investment. In this study, the rooftop PV systems from many high-volume installed PV systems countries and regions were collected to evaluate the lifetime energy yield of these systems based on machine learning techniques. Then, we obtained an association between the lifetime energy yield and technical configuration details of PV such as rated solar panel power, number of panels, rated inverter power, and number of inverters. Our findings reveal that the variability of PV lifetime energy is partly explained by the difference in PV system configuration. Indeed, our machine learning model can explain approximately 31 % ( 95 % confidence interval: 29–38%) of the variant energy efficiency of the PV system, given the configuration and components of the PV system. Our study has contributed useful knowledge to support the planning and design of a rooftop PV system such as PV financial modeling and PV investment decision.

Suggested Citation

  • Ngoc Thien Le & Watit Benjapolakul, 2019. "Evaluation of Contribution of PV Array and Inverter Configurations to Rooftop PV System Energy Yield Using Machine Learning Techniques," Energies, MDPI, vol. 12(16), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:16:p:3158-:d:258309
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/16/3158/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/16/3158/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Suomalainen, Kiti & Wang, Vincent & Sharp, Basil, 2017. "Rooftop solar potential based on LiDAR data: Bottom-up assessment at neighbourhood level," Renewable Energy, Elsevier, vol. 111(C), pages 463-475.
    2. He, Fanbo & Zhao, Zhengming & Yuan, Liqiang, 2012. "Impact of inverter configuration on energy cost of grid-connected photovoltaic systems," Renewable Energy, Elsevier, vol. 41(C), pages 328-335.
    3. Damian Shaw-Williams & Connie Susilawati & Geoffrey Walker, 2018. "Value of Residential Investment in Photovoltaics and Batteries in Networks: A Techno-Economic Analysis," Energies, MDPI, vol. 11(4), pages 1-25, April.
    4. J. P. Royston, 1982. "An Extension of Shapiro and Wilk's W Test for Normality to Large Samples," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(2), pages 115-124, June.
    5. Bhandari, Khagendra P. & Collier, Jennifer M. & Ellingson, Randy J. & Apul, Defne S., 2015. "Energy payback time (EPBT) and energy return on energy invested (EROI) of solar photovoltaic systems: A systematic review and meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 133-141.
    6. J. P. Royston, 1982. "The W Test for Normality," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(2), pages 176-180, June.
    7. Chaianong, Aksornchan & Tongsopit, Sopitsuda & Bangviwat, Athikom & Menke, Christoph, 2019. "Bill saving analysis of rooftop PV customers and policy implications for Thailand," Renewable Energy, Elsevier, vol. 131(C), pages 422-434.
    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. Yu Fujimoto & Akihisa Kaneko & Yutaka Iino & Hideo Ishii & Yasuhiro Hayashi, 2023. "Challenges in Smartizing Operational Management of Functionally-Smart Inverters for Distributed Energy Resources: A Review on Machine Learning Aspects," Energies, MDPI, vol. 16(3), pages 1-26, January.
    2. Isaac Machorro-Cano & Giner Alor-Hernández & Mario Andrés Paredes-Valverde & Lisbeth Rodríguez-Mazahua & José Luis Sánchez-Cervantes & José Oscar Olmedo-Aguirre, 2020. "HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving," Energies, MDPI, vol. 13(5), pages 1-24, March.
    3. Chih-Chiang Wei, 2019. "Evaluation of Photovoltaic Power Generation by Using Deep Learning in Solar Panels Installed in Buildings," Energies, MDPI, vol. 12(18), pages 1-18, 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. Minguez, Ana & Javier Sese, F., 2022. "Why do you want a relationship, anyway? Consent to receive marketing communications and donors’ willingness to engage with nonprofits," Journal of Business Research, Elsevier, vol. 148(C), pages 356-367.
    2. Lauren Bin Dong & David E. A. Giles, 2004. "An Empirical Likelihood Ratio Test for Normality," Econometrics Working Papers 0401, Department of Economics, University of Victoria.
    3. Tomasz Górecki & Lajos Horváth & Piotr Kokoszka, 2020. "Tests of Normality of Functional Data," International Statistical Review, International Statistical Institute, vol. 88(3), pages 677-697, December.
    4. Amir Abolhassani & Gale Boyd & Majid Jaridi & Bhaskaran Gopalakrishnan & James Harner, 2023. "“Is Energy That Different from Labor?” Similarity in Determinants of Intensity for Auto Assembly Plants," Energies, MDPI, vol. 16(4), pages 1-35, February.
    5. Jurgita Arnastauskaitė & Tomas Ruzgas & Mindaugas Bražėnas, 2021. "A New Goodness of Fit Test for Multivariate Normality and Comparative Simulation Study," Mathematics, MDPI, vol. 9(23), pages 1-20, November.
    6. Jurgen A. Doornik & Henrik Hansen, 2008. "An Omnibus Test for Univariate and Multivariate Normality," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 927-939, December.
    7. Peter Gerbrands & Brigitte Unger & Joras Ferwerda, 2022. "Bilateral responsive regulation and international tax competition: An agent‐based simulation," Regulation & Governance, John Wiley & Sons, vol. 16(3), pages 760-780, July.
    8. Keith Pilbeam & Hamish Preston, 2019. "An Empirical Investigation of the Performance of Japanese Mutual Funds: Skill or Luck?," IJFS, MDPI, vol. 7(1), pages 1-16, January.
    9. Hazzar Habbib & Julien Verzeaux & Elodie Nivelle & David Roger & Jérôme Lacoux & Manuella Catterou & Bertrand Hirel & Frédéric Dubois & Thierry Tétu, 2016. "Conversion to No-Till Improves Maize Nitrogen Use Efficiency in a Continuous Cover Cropping System," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-16, October.
    10. Ioan Ianoş & Igor Sîrodoev & Gabriel Pascariu & Geoffrey Henebry, 2016. "Divergent patterns of built-up urban space growth following post-socialist changes," Urban Studies, Urban Studies Journal Limited, vol. 53(15), pages 3172-3188, November.
    11. Joseph Aharony & Chan†Jane Lin & Martin P. Loeb, 1993. "Initial Public Offerings, Accounting Choices, and Earnings Management," Contemporary Accounting Research, John Wiley & Sons, vol. 10(1), pages 61-81, September.
    12. Orgeta Gjermëni, 2017. "Temporal Statistical Analysis of Degree Distributions in an Undirected Landline Phone Call Network Graph Series," Data, MDPI, vol. 2(4), pages 1-10, October.
    13. Prăvălie, Remus & Sîrodoev, Igor & Patriche, Cristian & Roșca, Bogdan & Piticar, Adrian & Bandoc, Georgeta & Sfîcă, Lucian & Tişcovschi, Adrian & Dumitraşcu, Monica & Chifiriuc, Carmen & Mănoiu, Valen, 2020. "The impact of climate change on agricultural productivity in Romania. A country-scale assessment based on the relationship between climatic water balance and maize yields in recent decades," Agricultural Systems, Elsevier, vol. 179(C).
    14. V.A. Byvshev & N.E. Brovkina, 2018. "Credit Market and Economic Growth of Russia: Modeling Mutual Influence," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 622-636.
    15. Sebastian Klaudiusz Tomczak, 2021. "Ratio Selection between Six Sectors in the Visegrad Group Using Parametric and Nonparametric ANOVA," Energies, MDPI, vol. 14(21), pages 1-20, November.
    16. Owusu Benjamin, 2021. "Fiscal Sustainability Hypothesis Test in Central and Eastern Europe: A Panel Data Perspective," Central European Economic Journal, Sciendo, vol. 8(55), pages 285-312, January.
    17. Dimitar Bozalakov & Mohannad J. Mnati & Joannes Laveyne & Jan Desmet & Lieven Vandevelde, 2019. "Battery Storage Integration in Voltage Unbalance and Overvoltage Mitigation Control Strategies and Its Impact on the Power Quality," Energies, MDPI, vol. 12(8), pages 1-26, April.
    18. Lada, Emily K. & Wilson, James R., 2006. "A wavelet-based spectral procedure for steady-state simulation analysis," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1769-1801, November.
    19. Iolanda Saviuc & Herbert Peremans & Steven Van Passel & Kevin Milis, 2019. "Economic Performance of Using Batteries in European Residential Microgrids under the Net-Metering Scheme," Energies, MDPI, vol. 12(1), pages 1-28, January.
    20. Gustavo Castilho Beruski & Luis Miguel Schiebelbein & André Belmont Pereira, 2020. "Maize Yield Components as Affected by Plant Population, Planting Date and Soil Coverings in Brazil," Agriculture, MDPI, vol. 10(12), pages 1-20, November.

    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:12:y:2019:i:16:p:3158-:d:258309. 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.