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Wild Horse Optimization with Deep Learning-Driven Short-Term Load Forecasting Scheme for Smart Grids

Author

Listed:
  • Abdelwahed Motwakel

    (Department of Information Systems, College of Business Administration in Hawtat Bani Tamim, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia)

  • Eatedal Alabdulkreem

    (Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia)

  • Abdulbaset Gaddah

    (Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Mecca 24382, Saudi Arabia)

  • Radwa Marzouk

    (Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia)

  • Nermin M. Salem

    (Department of Electrical Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, Egypt)

  • Abu Sarwar Zamani

    (Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia)

  • Amgad Atta Abdelmageed

    (Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia)

  • Mohamed I. Eldesouki

    (Department of Information System, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia)

Abstract

Energy is a major driver of human activity. Demand response is of the utmost importance to maintain the efficient and reliable operation of smart grid systems. The short-term load forecasting (STLF) method is particularly significant for electric fields in the trade of energy. This model has several applications to everyday operations of electric utilities, namely load switching, energy-generation planning, contract evaluation, energy purchasing, and infrastructure maintenance. A considerable number of STLF algorithms have introduced a tradeoff between convergence rate and forecast accuracy. This study presents a new wild horse optimization method with a deep learning-based STLF scheme (WHODL-STLFS) for SGs. The presented WHODL-STLFS technique was initially used for the design of a WHO algorithm for the optimal selection of features from the electricity data. In addition, attention-based long short-term memory (ALSTM) was exploited for learning the energy consumption behaviors to forecast the load. Finally, an artificial algae optimization (AAO) algorithm was applied as the hyperparameter optimizer of the ALSTM model. The experimental validation process was carried out on an FE grid and a Dayton grid and the obtained results indicated that the WHODL-STLFS technique achieved accurate load-prediction performance in SGs.

Suggested Citation

  • Abdelwahed Motwakel & Eatedal Alabdulkreem & Abdulbaset Gaddah & Radwa Marzouk & Nermin M. Salem & Abu Sarwar Zamani & Amgad Atta Abdelmageed & Mohamed I. Eldesouki, 2023. "Wild Horse Optimization with Deep Learning-Driven Short-Term Load Forecasting Scheme for Smart Grids," Sustainability, MDPI, vol. 15(2), pages 1-17, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1524-:d:1034138
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    References listed on IDEAS

    as
    1. Hafeez, Ghulam & Alimgeer, Khurram Saleem & Khan, Imran, 2020. "Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid," Applied Energy, Elsevier, vol. 269(C).
    2. Waqas Ahmad & Nasir Ayub & Tariq Ali & Muhammad Irfan & Muhammad Awais & Muhammad Shiraz & Adam Glowacz, 2020. "Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine," Energies, MDPI, vol. 13(11), pages 1-17, June.
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    Cited by:

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