IDEAS home Printed from https://ideas.repec.org/a/gam/jforec/v7y2024i1p1-d1553856.html
   My bibliography  Save this article

Comparative Analysis of Supervised Learning Techniques for Forecasting PV Current in South Africa

Author

Listed:
  • Ely Ondo Ekogha

    (Department of Computer System Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

  • Pius A. Owolawi

    (Department of Computer System Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

Abstract

The fluctuations in solar irradiance and temperature throughout the year require an accurate methodology for forecasting the generated current of a PV system based on its specifications. The optimal technique must effectively manage rapid weather fluctuations while maintaining high accuracy in forecasting the performance of a PV panel. This work presents a comparative examination of supervised learning algorithms optimized with particle swarm optimization for estimating photovoltaic output current. The empirical formula’s measured currents are compared with outputs from various neural networks techniques, including feedforward neural networks (FFNNs), the general regression network known as GRNN, cascade forward neural networks also known as CFNNs, and adaptive fuzzy inference systems known as ANFISs, all optimized for enhanced accuracy using the particle swarm optimization (PSO) method. The ground data utilized for these models comprises hourly irradiations and temperatures from 2023, sourced from several places in South Africa. The accuracy levels indicated by statistical error margins from the root mean square error (RMSE), mean bias error (MBE), and mean absolute percentage error (MAPE) imply a universal enhancement in the algorithms’ precision upon optimization.

Suggested Citation

  • Ely Ondo Ekogha & Pius A. Owolawi, 2024. "Comparative Analysis of Supervised Learning Techniques for Forecasting PV Current in South Africa," Forecasting, MDPI, vol. 7(1), pages 1-20, December.
    • Handle: RePEc:gam:jforec:v:7:y:2024:i:1:p:1-:d:1553856
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-9394/7/1/1/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-9394/7/1/1/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Halabi, Laith M. & Mekhilef, Saad & Hossain, Monowar, 2018. "Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation," Applied Energy, Elsevier, vol. 213(C), pages 247-261.
    2. Xiang, Zheng & Magnini, Vincent P. & Fesenmaier, Daniel R., 2015. "Information technology and consumer behavior in travel and tourism: Insights from travel planning using the internet," Journal of Retailing and Consumer Services, Elsevier, vol. 22(C), pages 244-249.
    3. Theocharides, Spyros & Makrides, George & Livera, Andreas & Theristis, Marios & Kaimakis, Paris & Georghiou, George E., 2020. "Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing," Applied Energy, Elsevier, vol. 268(C).
    Full references (including those not matched with items on IDEAS)

    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. Liu, Chih-Hsing & Dong, Tse-Ping & Vu, Ho Tran, 2023. "Transformed virtual concepts into reality: Linkage the viewpoint of entrepreneurial passion, technology adoption propensity and advantage to usage intention," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    2. Shiwei Shen & Marios Sotiriadis & Qing Zhou, 2020. "Could Smart Tourists Be Sustainable and Responsible as Well? The Contribution of Social Networking Sites to Improving Their Sustainable and Responsible Behavior," Sustainability, MDPI, vol. 12(4), pages 1-21, February.
    3. Zheng, Lingwei & Su, Ran & Sun, Xinyu & Guo, Siqi, 2023. "Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output," Energy, Elsevier, vol. 271(C).
    4. Qu, Zhijian & Xu, Juan & Wang, Zixiao & Chi, Rui & Liu, Hanxin, 2021. "Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method," Energy, Elsevier, vol. 227(C).
    5. Yoo, Chul Woo & Goo, Jahyun & Huang, C. Derrick & Nam, Kichan & Woo, Mina, 2017. "Improving travel decision support satisfaction with smart tourism technologies: A framework of tourist elaboration likelihood and self-efficacy," Technological Forecasting and Social Change, Elsevier, vol. 123(C), pages 330-341.
    6. Angel Stoykov, 2023. "The Influence Of Social Media On Advertising Tourism Services (In The Example Of Bulgaria)," Business Management, D. A. Tsenov Academy of Economics, Svishtov, Bulgaria, issue 1 Year 20, pages 64-80.
    7. Farzana Sharmin & Mohammad Tipu Sultan & Alina Badulescu & Dorin Paul Bac & Benqian Li, 2020. "Millennial Tourists’ Environmentally Sustainable Behavior Towards a Natural Protected Area: An Integrative Framework," Sustainability, MDPI, vol. 12(20), pages 1-24, October.
    8. Derong Lv & Guojiang Xiong & Xiaofan Fu & Yang Wu & Sheng Xu & Hao Chen, 2022. "Optimal Power Flow with Stochastic Solar Power Using Clustering-Based Multi-Objective Differential Evolution," Energies, MDPI, vol. 15(24), pages 1-21, December.
    9. Jayesh Thaker & Robert Höller, 2022. "A Comparative Study of Time Series Forecasting of Solar Energy Based on Irradiance Classification," Energies, MDPI, vol. 15(8), pages 1-26, April.
    10. Norbert Haydam & Theodor Purcarea & Tudor Edu & Iliuta Costel Negricea, 2017. "Explaining Satisfaction at a Foreign Tourism Destination – an Intra-Generational Approach. Evidence within Generation Y from South Africa and Romania," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 19(45), pages 528-528, May.
    11. 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.
    12. Suzanne Amaro & Paulo Duarte, 2017. "Social media use for travel purposes: a cross cultural comparison between Portugal and the UK," Information Technology & Tourism, Springer, vol. 17(2), pages 161-181, June.
    13. A Fronzetti Colladon & B Guardabascio & R Innarella, 2021. "Using social network and semantic analysis to analyze online travel forums and forecast tourism demand," Papers 2105.07727, arXiv.org.
    14. Mohammed Jabreel & Assumpció Huertas & Antonio Moreno, 2018. "Semantic analysis and the evolution towards participative branding: Do locals communicate the same destination brand values as DMOs?," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-29, November.
    15. Duro, Juan Antonio & Osório, António & Perez-Laborda, Alejandro, 2024. "Modeling uncertainty in tourism markets," Annals of Tourism Research, Elsevier, vol. 107(C).
    16. Hossein Moayedi & Amir Mosavi, 2021. "An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework," Energies, MDPI, vol. 14(4), pages 1-18, February.
    17. Leidy Gutiérrez & Julian Patiño & Eduardo Duque-Grisales, 2021. "A Comparison of the Performance of Supervised Learning Algorithms for Solar Power Prediction," Energies, MDPI, vol. 14(15), pages 1-16, July.
    18. Vinaitheerthan Renganathan & Amitabh Upadhya, 2021. "Dubai Restaurants: A Sentiment Analysis of Tourist Reviews," Academica Turistica - Tourism and Innovation Journal, University of Primorska Press, vol. 14(2), pages 165-174.
    19. Aneeta Rehman & Shahid Yaqoob, 2022. "Religious Beliefs about Concept of Qalb (Heart) for Innovative Transformational Learning," Journal of Policy Research (JPR), Research Foundation for Humanity (RFH), vol. 8(3), pages 35-45, December.
    20. Ahmed Aljanad & Nadia M. L. Tan & Vassilios G. Agelidis & Hussain Shareef, 2021. "Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 14(4), pages 1-20, February.

    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:jforec:v:7:y:2024:i:1:p:1-:d:1553856. 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.