Machine learning for power outage prediction during hurricanes: An extensive review

Kehkashan Fatima, Hussain Shareef, Flavio Bezerra Costa, Abdullah Akram Bajwa, Ling Ai Wong

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The surge of machine learning (ML) applications and increasing usage of data driven approach for resilience enhancement provide great opportunities for applying ML techniques to power outage prediction (POP) during hurricanes. Considering the substantial damage that wind hazards can cause to power system network (PSN) in countries such as the USA and China, researchers have devised numerous approaches for disaster prevention to reduce their susceptibility. Since the selection of optimal ML algorithms for POP during hurricanes is a complex task, this paper helps researchers easily comprehend the numerous prediction techniques used worldwide for power outages during hurricanes. The paper provides a brief review of several categories of hurricanes and the damage it causes to the PSN more specifically to distribution side, the necessary measures to improve the PSN resilience, and finally a sequential approach for optimal ML model selection techniques that researchers and engineers can use to improve the POP during hurricanes. In this study the effectiveness of ML algorithms has been explored with the help of performance evaluation metrics which serve as the basic deciding criteria for optimal model selection. This research also highlights some of the key issues and challenges of integrating machine learning algorithms with existing PSN.

Original languageEnglish
Article number108056
JournalEngineering Applications of Artificial Intelligence
Volume133
DOIs
Publication statusPublished - Jul 2024

Keywords

  • Hurricane
  • Machine learning application
  • Power outage prediction
  • Power system resilience

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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