Session Materials

Leveraging Machine Learning for Multi-Step Failure Forecasting in RTU Analog Modules and Estimating Key Performance Indicators to Support Management Decision-Making

Ref D2-11009-2024 • 2024
  • Price for member: 20 €
  • Price for non member: 30 €
Leveraging Machine Learning for Multi-Step Failure Forecasting in RTU Analog Modules and Estimating Key Performance Indicators to Support Management Decision-Making

Summary

Session Delegates (Members and non-members): access these materials for free via your Session registration account.

Additional informations

Publication type Session Materials
Reference D2-11009-2024
Publication year
Publisher CIGRE
Study committees
  • Information Systems, Telecommunications and Cybersecurity (D2)
File size 2 MB
Price for non member 30 €
Price for member 20 €

Authors

Daniel FELIP, Eduardo CORONEL

Keywords

Hyperparameter tuning, Key Performance Indicators estimation, Machine Learning Regression algorithm, Management decision-making support, Multi-step annual Failure Forecasting, Remote Terminal Unit analog modules

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Leveraging Machine Learning for Multi-Step Failure Forecasting in RTU Analog Modules and Estimating Key Performance Indicators to Support Management Decision-Making
Leveraging Machine Learning for Multi-Step Failure Forecasting in RTU Analog Modules and Estimating Key Performance Indicators to Support Management Decision-Making