IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i16p12622-d1221420.html
   My bibliography  Save this article

Towards Sustainable Energy Grids: A Machine Learning-Based Ensemble Methods Approach for Outages Estimation in Extreme Weather Events

Author

Listed:
  • Ulaa AlHaddad

    (Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University (KAU), Jeddah 21589, Saudi Arabia)

  • Abdullah Basuhail

    (Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University (KAU), Jeddah 21589, Saudi Arabia)

  • Maher Khemakhem

    (Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University (KAU), Jeddah 21589, Saudi Arabia)

  • Fathy Elbouraey Eassa

    (Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University (KAU), Jeddah 21589, Saudi Arabia)

  • Kamal Jambi

    (Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University (KAU), Jeddah 21589, Saudi Arabia)

Abstract

The critical challenge of enhancing the resilience and sustainability of energy management systems has arisen due to historical outages. A potentially effective strategy for addressing outages in energy grids involves preparing for future failures resulting from line vulnerability or grid disruptions. As a result, many researchers have undertaken investigations to develop machine learning-based methodologies for outage forecasting for smart grids. This research paper proposed applying ensemble methods to forecast the conditions of smart grid devices during extreme weather events to enhance the resilience of energy grids. In this study, we evaluate the efficacy of five machine learning algorithms, namely support vector machines (SVM), artificial neural networks (ANN), logistic regression (LR), decision tree (DT), and Naive Bayes (NB), by utilizing the bagging ensemble technique. The results demonstrate a remarkable accuracy rate of 99.98%, with a true positive rate of 99.6% and a false positive rate of 0.01%. This research establishes a foundation for implementing sustainable energy integration into electrical networks by accurately predicting the occurrence of damaged components in the energy grid caused by extreme weather events. Moreover, it enables operators to manage the energy generated effectively and facilitates the achievement of energy production efficiency. Our research contributes to energy management systems using ensemble methods to predict grid vulnerabilities. This advancement lays the foundation for developing resilient and dependable energy infrastructure capable of withstanding unfavorable weather conditions and assisting in achieving energy production efficiency goals.

Suggested Citation

  • Ulaa AlHaddad & Abdullah Basuhail & Maher Khemakhem & Fathy Elbouraey Eassa & Kamal Jambi, 2023. "Towards Sustainable Energy Grids: A Machine Learning-Based Ensemble Methods Approach for Outages Estimation in Extreme Weather Events," Sustainability, MDPI, vol. 15(16), pages 1-19, August.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:16:p:12622-:d:1221420
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/16/12622/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/16/12622/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Linas Martišauskas & Juozas Augutis & Ričardas Krikštolaitis & Rolandas Urbonas & Inga Šarūnienė & Vytis Kopustinskas, 2022. "A Framework to Assess the Resilience of Energy Systems Based on Quantitative Indicators," Energies, MDPI, vol. 15(11), pages 1-25, May.
    2. Fahad M. Almasoudi, 2023. "Enhancing Power Grid Resilience through Real-Time Fault Detection and Remediation Using Advanced Hybrid Machine Learning Models," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
    3. Wang, Chong & Ju, Ping & Wu, Feng & Pan, Xueping & Wang, Zhaoyu, 2022. "A systematic review on power system resilience from the perspective of generation, network, and load," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    4. Liu, Hanchen & Wang, Chong & Ju, Ping & Li, Hongyu, 2022. "A sequentially preventive model enhancing power system resilience against extreme-weather-triggered failures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    5. Hossain, Eklas & Roy, Shidhartho & Mohammad, Naeem & Nawar, Nafiu & Dipta, Debopriya Roy, 2021. "Metrics and enhancement strategies for grid resilience and reliability during natural disasters," Applied Energy, Elsevier, vol. 290(C).
    6. Molyneaux, Lynette & Wagner, Liam & Froome, Craig & Foster, John, 2012. "Resilience and electricity systems: A comparative analysis," Energy Policy, Elsevier, vol. 47(C), pages 188-201.
    7. Branscomb, Lewis M., 2006. "Sustainable cities: Safety and security," Technology in Society, Elsevier, vol. 28(1), pages 225-234.
    8. Raza, Muhammad Qamar & Khosravi, Abbas, 2015. "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1352-1372.
    9. Roshanak Nateghi & Seth Guikema & Steven Quiring, 2014. "Forecasting hurricane-induced power outage durations," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(3), pages 1795-1811, December.
    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. Qian, Lanping & Bai, Yang & Wang, Wenya & Meng, Fanyi & Chen, Zhisong, 2023. "Natural gas crisis, system resilience and emergency responses: A China case," Energy, Elsevier, vol. 276(C).
    2. Benjamin McLellan & Qi Zhang & Hooman Farzaneh & N. Agya Utama & Keiichi N. Ishihara, 2012. "Resilience, Sustainability and Risk Management: A Focus on Energy," Challenges, MDPI, vol. 3(2), pages 1-30, August.
    3. Hou, Hui & Tang, Junyi & Zhang, Zhiwei & Wang, Zhuo & Wei, Ruizeng & Wang, Lei & He, Huan & Wu, Xixiu, 2023. "Resilience enhancement of distribution network under typhoon disaster based on two-stage stochastic programming," Applied Energy, Elsevier, vol. 338(C).
    4. Das, Laya & Munikoti, Sai & Natarajan, Balasubramaniam & Srinivasan, Babji, 2020. "Measuring smart grid resilience: Methods, challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    5. Schlör, Holger & Venghaus, Sandra & Hake, Jürgen-Friedrich, 2018. "The FEW-Nexus city index – Measuring urban resilience," Applied Energy, Elsevier, vol. 210(C), pages 382-392.
    6. Hou, Hui & Liu, Chao & Wei, Ruizeng & He, Huan & Wang, Lei & Li, Weibo, 2023. "Outage duration prediction under typhoon disaster with stacking ensemble learning," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    7. Govindan, Rajesh & Al-Ansari, Tareq, 2019. "Computational decision framework for enhancing resilience of the energy, water and food nexus in risky environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 653-668.
    8. Dunn, Laurel N. & Sohn, Michael D. & LaCommare, Kristina Hamachi & Eto, Joseph H., 2019. "Exploratory analysis of high-resolution power interruption data reveals spatial and temporal heterogeneity in electric grid reliability," Energy Policy, Elsevier, vol. 129(C), pages 206-214.
    9. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    10. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    11. Jesus Beyza & Jose M. Yusta, 2021. "Integrated Risk Assessment for Robustness Evaluation and Resilience Optimisation of Power Systems after Cascading Failures," Energies, MDPI, vol. 14(7), pages 1-18, April.
    12. Mazur, Christoph & Hoegerle, Yannick & Brucoli, Maria & van Dam, Koen & Guo, Miao & Markides, Christos N. & Shah, Nilay, 2019. "A holistic resilience framework development for rural power systems in emerging economies," Applied Energy, Elsevier, vol. 235(C), pages 219-232.
    13. V. Y. Kondaiah & B. Saravanan, 2022. "Short-Term Load Forecasting with a Novel Wavelet-Based Ensemble Method," Energies, MDPI, vol. 15(14), pages 1-17, July.
    14. Alain Aoun & Mehdi Adda & Adrian Ilinca & Mazen Ghandour & Hussein Ibrahim, 2024. "Centralized vs. Decentralized Electric Grid Resilience Analysis Using Leontief’s Input–Output Model," Energies, MDPI, vol. 17(6), pages 1-21, March.
    15. Namrye Son, 2021. "Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
    16. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    17. Molyneaux, Lynette & Froome, Craig & Wagner, Liam & Foster, John, 2013. "Australian power: Can renewable technologies change the dominant industry view?," Renewable Energy, Elsevier, vol. 60(C), pages 215-221.
    18. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    19. Verstraete, Gylian & Aghezzaf, El-Houssaine & Desmet, Bram, 2019. "A data-driven framework for predicting weather impact on high-volume low-margin retail products," Journal of Retailing and Consumer Services, Elsevier, vol. 48(C), pages 169-177.
    20. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.

    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:jsusta:v:15:y:2023:i:16:p:12622-:d:1221420. 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.