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Integrated Multi Scale Data Analytics and Machine Learning for the Grid

This projects overarching goal is to create advanced, distributed data analytics capability within the DOE GM Consortium, to provide visibility, and controllability to distribution grid and building operators.   While there has been significant development of analytics methods with streaming data at the transmission level, distribution and buildings level analytics are in the elementary stages. Machine learning is the basis for the analysis development, and will allow new levels of visibility and resource integration to be achieved using open and utility datasets at both the building and distribution level.  This project will develop the necessary framework to promote and integrate a larger distributed analytics activity utilizing existing and new data sources developed through both private and DOE partnerships.

The project will seek to demonstrate the full capability of synchronized disparate data sources for distribution and building grid analysis and control with application of machine learning, enabling future distributed applications such as transactional energy validation, fault analytics and failure prediction, resilience applications and a BMS/DMS integration methodology for transition to industry. These use cases are being identified as having multi program application, along with a key position in the modernized grid where advanced analytics could be transformational to performance and accuracy.

Project Quick Facts

Topic ID: 1.4.09
Funding : $4 Million
Duration: 3 Years

Technical Project Team

  • Lead

    Emma Stewart, LLNL

  • Michael Chertkov,

Project Partners

PSL Energy Services
National Instruments
Rochester Public Utilities (RPU)
Florida Power and Light

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The Grid Modernization Laboratory Consortium is a strategic partnership between the U.S. Department of Energy and 13 National Laboratories to bring together leading experts and resources. If you would like to partner with GMLC, contact us at the link below.

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