SOURCE-LINKED INTELLIGENCE

Trust-ML: An Optimization-based Platform for Building Trust in Machine Learning Models used for Power Systems

CORDIS · observation · Publication date unknown

Trust-ML: An Optimization-based Platform for Building Trust in Machine Learning Models used for Power Systems Deep decarbonization of the energy sector will require massive penetration of stochastic renewable energy resources and an enormous amount of grid asset coordination; this represents a challenging paradigm for power system operators. With its ability to learn in complex environments and provide predictive solutions on fast timescales, machine learning (ML) is posed to help overcome these challenges and dramatically transform power systems in coming decades. Emerging EU verification standards, however, will require that all ML and Reinforcement Learning (RL) used in safety critical applications be demonstrably trustworthy. In this project, we develop a unified framework, known as Trust-ML, for assessing the quantitative trustworthiness of the neural network models commonly used in power systems. Trust-ML uses a novel, convex optimization appr

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recordType

award

status

TERMINATED

region

EU

value

230774.4

unit

EUR