Veritone is accelerating the world’s transition to more sustainable, reliable, and affordable energy. Our customers include utilities, independent power producers, and developers who use our software to optimize, synchronize, and intelligently control macro and micro grids. Veritone Energy Solutions use patented AI technology and the Veritone aiWARE operating system for AI to make clean energy more predictable, cost effective and resilient, accelerating the mission to end global dependence on fossil fuels. Veritone’s patented, real-time dynamic modeling and predictive device control enables autonomous microgrid management, optimizing smart grid energy distribution by continuously knowing how much energy to deliver from which asset. This ground-breaking technology ensures consistent, cost-effective clean energy during normal operations and grid resilience in the face of the unexpected.
Learn how artificial intelligence and a renewable energy powered microgrid can reduce the carbon footprint of one of the dirtiest industries – cryptocurrency.
In this white paper, you’ll learn how AI can solve the unique challenges of a modern, complex, renewable electric smart grid in real-time and at scale.
Microgrids are continuing to drive the transformation and decentralization of our energy grid, but planning and managing microgrids can be complex. A new white paper from Veritone...
As electric power systems diversify and rely more heavily on renewable sources, energy storage, and microgrids, the grid will become more highly distributed, dynamic, and resilient...
Veritone presents five artificial intelligence-powered solutions that help those in the electric power industry enhance grid resilience, increase the rate of decarbonization, ...
Veritone describes the distributed microgrid management and control system they developed with Microsoft that reduces energy costs and the carbon footprint of DER / renewables...
Veritone shows how their patented technology, Cooperative Distributed Inferencing (CDI), delivers resilience and grid optimization through real-time dynamic grid modeling.
