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Temporal forecasting by converting stochastic behaviour into a stable pattern in electric grid

Author

Listed:
  • Akram Qashou

    (Anglia Ruskin University)

  • Sufian Yousef

    (Anglia Ruskin University)

  • Firas Hazzaa

    (Anglia Ruskin University)

  • Kahtan Aziz

    (Anglia Ruskin University)

Abstract

The malfunction variables of power stations are related to the areas of weather, physical structure, control, and load behavior. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behavior into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by long-short-term memory and gated recurrent unit algorithms are used to perform the short-term estimation. The environment, the operation, and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a dataset. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, to address the gap in knowledge for any future power grid estimated failures, the achieved results in this paper form good basis for a testbed to estimate any grid future failures.

Suggested Citation

  • Akram Qashou & Sufian Yousef & Firas Hazzaa & Kahtan Aziz, 2024. "Temporal forecasting by converting stochastic behaviour into a stable pattern in electric grid," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(9), pages 4426-4442, September.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:9:d:10.1007_s13198-024-02454-0
    DOI: 10.1007/s13198-024-02454-0
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    References listed on IDEAS

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    1. Akram Qashou & Sufian Yousef & Erika Sanchez-Velazquez, 2022. "Mining sensor data in a smart environment: a study of control algorithms and microgrid testbed for temporal forecasting and patterns of failure," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2371-2390, October.
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