reviewEnergiesMay 11, 2023GOLD OA

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

Czech University of Life Sciences Prague · Bauman Moscow State Technical University

Indexed incrossrefdoaj

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 recent years due to their ability to handle large amounts of data and to make accurate predictions. The importance of these methods gained particular momentum with the recent transformation that the traditional power system underwent as they are morphing into the smart power grids of the future. The transition towards the smart grids that embed the high-renewables electricity systems is challenging, as the generation of…

Citation impact

179
total citations
FWCI
22.06
Percentile
100%
References
242
Citations per year

Authors

5

Topics & keywords

Keywords
  • Computer science
  • Electric power system
  • Renewable energy
  • Smart grid
  • Big data
  • Machine learning
  • Predictive power
  • Artificial intelligence
UN Sustainable Development Goals
  • Affordable and clean energy
No related works found for this paper.

Funding