PG&E leverages machine-learning and data science for asset management and DER integration

Utilities house enormous datasets that defy traditional analysis, for which machine-learning could be of great benefit. When machine-learning is applied to IoT data, utility companies are able to realise the next generation power grid that can eventually handle billions of endpoints on utility networks autonomously. Pacific Gas and Electric's (PG&E) emerging technologies leader Tom Martin and Paul Doherty, corporate relations, discuss how machine learning and data science is being leveraged for asset maintenance and the integration of distributed energy resources (DER). MSEI: What does machine-learning mean to PG&E? What is your definition of machine-learning? TM: Machine-learning at PG&E is the ability to use analytics to drive optimisation in our operations.