IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i2p525-d1323736.html
   My bibliography  Save this article

Performance Evaluation of Multiple Machine Learning Models in Predicting Power Generation for a Grid-Connected 300 MW Solar Farm

Author

Listed:
  • Obaid Aldosari

    (Department of Electrical Engineering, Prince Sattam Bin Abdulaziz University, Wadi Addawaser 11991, Saudi Arabia
    These authors contributed equally to this work.)

  • Salem Batiyah

    (Department of Electrical and Electronics Engineering Technology, Yanbu Industrial College, Yanbu Industrial 46452, Saudi Arabia
    These authors contributed equally to this work.)

  • Murtada Elbashir

    (Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia)

  • Waleed Alhosaini

    (Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

  • Kanagaraj Nallaiyagounder

    (Department of Electrical Engineering, Prince Sattam Bin Abdulaziz University, Wadi Addawaser 11991, Saudi Arabia)

Abstract

Integrating renewable energy sources (RES), such as photovoltaic (PV) systems, into power system networks increases uncertainty, leading to practical challenges. Therefore, an accurate photovoltaic (PV) power prediction model is required to provide essential data that supports smooth power system operation. Hence, the work presented in this paper compares and discusses the results of different machine learning (ML) techniques in predicting the power produced by the 300 MW Sakaka PV Power Plant in the north of Saudi Arabia. The validation of the presented work is performed using real-world operational data obtained from the specified solar farm. Several performance measures, including accuracy, precision, recall, F1 Score, and mean square error (MSE), are used in this work to evaluate the performance of the different ML approaches and determine the most precise prediction model. The obtained results show that the Support Vector Machine (SVM) with a Radial basis function (RBF) is the most effective approach for optimizing solar power prediction in large-scale solar farms.

Suggested Citation

  • Obaid Aldosari & Salem Batiyah & Murtada Elbashir & Waleed Alhosaini & Kanagaraj Nallaiyagounder, 2024. "Performance Evaluation of Multiple Machine Learning Models in Predicting Power Generation for a Grid-Connected 300 MW Solar Farm," Energies, MDPI, vol. 17(2), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:525-:d:1323736
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/2/525/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/2/525/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sharadga, Hussein & Hajimirza, Shima & Balog, Robert S., 2020. "Time series forecasting of solar power generation for large-scale photovoltaic plants," Renewable Energy, Elsevier, vol. 150(C), pages 797-807.
    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. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
    2. Xilong Lin & Yisen Niu & Zixuan Yan & Lianglin Zou & Ping Tang & Jifeng Song, 2024. "Hybrid Photovoltaic Output Forecasting Model with Temporal Convolutional Network Using Maximal Information Coefficient and White Shark Optimizer," Sustainability, MDPI, vol. 16(14), pages 1-20, July.
    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. Yang, Yanru & Liu, Yu & Zhang, Yihang & Shu, Shaolong & Zheng, Junsheng, 2025. "DEST-GNN: A double-explored spatio-temporal graph neural network for multi-site intra-hour PV power forecasting," Applied Energy, Elsevier, vol. 378(PA).
    5. Hou, Guolian & Ke, Yin & Huang, Congzhi, 2021. "A flexible constant power generation scheme for photovoltaic system by error-based active disturbance rejection control and perturb & observe," Energy, Elsevier, vol. 237(C).
    6. 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).
    7. Hongchao Zhang & Tengteng Zhu, 2022. "Stacking Model for Photovoltaic-Power-Generation Prediction," Sustainability, MDPI, vol. 14(9), pages 1-16, May.
    8. Siamak Hoseinzadeh & Daniele Groppi & Adriana Scarlet Sferra & Umberto Di Matteo & Davide Astiaso Garcia, 2022. "The PRISMI Plus Toolkit Application to a Grid-Connected Mediterranean Island," Energies, MDPI, vol. 15(22), pages 1-14, November.
    9. Bashir, Hassan & Sibtain, Muhammad & Hanay, Özge & Azam, Muhammad Imran & Qurat-ul-Ain, & Saleem, Snoober, 2023. "Decomposition and Harris hawks optimized multivariate wind speed forecasting utilizing sequence2sequence-based spatiotemporal attention," Energy, Elsevier, vol. 278(PB).
    10. Wang, Min & Rao, Congjun & Xiao, Xinping & Hu, Zhuo & Goh, Mark, 2024. "Efficient shrinkage temporal convolutional network model for photovoltaic power prediction," Energy, Elsevier, vol. 297(C).
    11. Rafati, Amir & Joorabian, Mahmood & Mashhour, Elaheh & Shaker, Hamid Reza, 2021. "High dimensional very short-term solar power forecasting based on a data-driven heuristic method," Energy, Elsevier, vol. 219(C).
    12. Su, Huai & Chi, Lixun & Zio, Enrico & Li, Zhenlin & Fan, Lin & Yang, Zhe & Liu, Zhe & Zhang, Jinjun, 2021. "An integrated, systematic data-driven supply-demand side management method for smart integrated energy systems," Energy, Elsevier, vol. 235(C).
    13. Adar, Mustapha & Najih, Youssef & Gouskir, Mohamed & Chebak, Ahmed & Mabrouki, Mustapha & Bennouna, Amin, 2020. "Three PV plants performance analysis using the principal component analysis method," Energy, Elsevier, vol. 207(C).
    14. Gong, Yu & Liu, Pan & Ming, Bo & Li, Dingfang, 2021. "Identifying the effect of forecast uncertainties on hybrid power system operation: A case study of Longyangxia hydro–photovoltaic plant in China," Renewable Energy, Elsevier, vol. 178(C), pages 1303-1321.
    15. Bingqing Huang & Haonan Zheng & Xinbo Guo & Yi Yang & Ximing Liu, 2021. "A Novel Model Based on DA-RNN Network and Skip Gated Recurrent Neural Network for Periodic Time Series Forecasting," Sustainability, MDPI, vol. 14(1), pages 1-14, December.
    16. Gülsah Erdogan & Wiem Fekih Hassen, 2023. "Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations," Energies, MDPI, vol. 16(18), pages 1-29, September.
    17. Michael Meiser & Ingo Zinnikus, 2024. "A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities," Energies, MDPI, vol. 17(9), pages 1-29, April.
    18. Abubaker, Ahmad M. & Darwish Ahmad, Adnan & Salaimeh, Ahmad A. & Akafuah, Nelson K. & Saito, Kozo, 2022. "A novel solar combined cycle integration: An exergy-based optimization using artificial neural network," Renewable Energy, Elsevier, vol. 181(C), pages 914-932.
    19. Zhang, Mingyue & Han, Yang & Wang, Chaoyang & Yang, Ping & Wang, Congling & Zalhaf, Amr S., 2024. "Ultra-short-term photovoltaic power prediction based on similar day clustering and temporal convolutional network with bidirectional long short-term memory model: A case study using DKASC data," Applied Energy, Elsevier, vol. 375(C).
    20. Nabilah Mat Kassim & Sathiswary Santhiran & Ammar Ahmed Alkahtani & Mohammad Aminul Islam & Sieh Kiong Tiong & Mohd Yusrizal Mohd Yusof & Nowshad Amin, 2023. "An Adaptive Decision Tree Regression Modeling for the Output Power of Large-Scale Solar (LSS) Farm Forecasting," Sustainability, MDPI, vol. 15(18), pages 1-12, September.

    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:17:y:2024:i:2:p:525-:d:1323736. 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.