Machine Learning Approaches for Power System Parameters Prediction: A Systematic Review

Tolulope David Makanju, Thokozani Shongwe, Oluwole John Famoriji

Research output: Contribution to journalArticlepeer-review


Prediction in the power system network is very crucial as expansion is needed in the network. Several methods have been used to predict the load on a network, from short to long time load prediction, to ensure adequate planning for future use. Since the power system network is dynamic, other parameters, such as voltage and frequency prediction, are necessary for effective planning against contingencies. Also, most power systems are interconnected networks; using isolated variables to predict any part of the network tends to reduce prediction accuracy. This review analyzed different machine learning approaches used for load, frequency, and voltage prediction in power systems and proposed a machine learning predictive approach using network topology behavior as input variables to the model. The analysis of the proposed model was tested using a regression model, Decision tree regressor, and long short-term memory. The analysis results indicate that with network topology behavior as input to the model, the prediction will be more accurate than when isolated variables of a particular Bus in a network are used for prediction. This work suggests that network topology behavior data should be used for prediction in a power system network rather than the use of isolated data of a particular bus or exogenous data for prediction in a power system. Therefore, this research recommends that the accuracy of different predictive models be tested on power system parameters by hybridizing the network topology behavior dataset and the exogenous dataset.

Original languageEnglish
Pages (from-to)66646-66679
Number of pages34
JournalIEEE Access
Publication statusPublished - 2024


  • Frequency
  • load prediction
  • machine learning
  • power system
  • voltage prediction

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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