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
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
- FWCI
- 22.06
- Percentile
- 100%
- References
- 242
Authors
5- WSWadim StriełkowskiCorresponding
Czech University of Life Sciences Prague
- AVAndrey Vlasov
Bauman Moscow State Technical University
- KSKirill Selivanov
Bauman Moscow State Technical University
- KAKonstantin A. Muraviev
Bauman Moscow State Technical University
- VAVadim A. Shakhnov
Bauman Moscow State Technical University
Topics & keywords
- Computer science
- Electric power system
- Renewable energy
- Smart grid
- Big data
- Machine learning
- Predictive power
- Artificial intelligence
- Affordable and clean energy