Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review

Striełkowski, Wadim; Vlasov, Andrey; Selivanov, Kirill; A. Muraviev, Konstantin; A. Shakhnov, Vadim

doi:10.3390/en16104025

Public

Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review

Published in Energies • May 11, 2023

Authors:

Wadim Striełkowski

Andrey Vlasov

Kirill Selivanov

Abstract

The use of machine learning and data-driven methods for predictive analysis of power systems offers the potential to accurately predict and manage the behavior of these systems by utilizing large volumes of data generated from various sources. These methods have gained significant attention in recen...

Subject

Computer science

Electric power system

Renewable energy

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