Reinforcement Learning-Based Prediction of Alarm Significance in Marginally Operating Electrical Grids
Author:
Affiliation:
1. SDU Center for Energy Informatics, Maersk Mc-Kinney Moeller Institute, The Faculty of Engineering, University of Southern Denmark, Odense, Denmark
Funder
Leveraging Smart Meter Data to Optimize Grid Investments
Danish Energy Agency
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/9424/10492621/10398591.pdf?arnumber=10398591
Reference29 articles.
1. A Survey of Time-Series Prediction for Digitally Enabled Maintenance of Electrical Grids
2. Modernizing the Grid: Challenges and Opportunities for a Sustainable Future
3. Optimal investment and scheduling of distributed energy resources with uncertainty in electric vehicle driving schedules
4. The Current State of the Art in Research on Predictive Maintenance in Smart Grid Distribution Network: Fault’s Types, Causes, and Prediction Methods—A Systematic Review
5. Validating Clustering Frameworks for Electric Load Demand Profiles
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