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Electric Utilities: An Industry in Desperate Need of a Data Strategy
Governments around the world are creating plans to fight climate change by reducing dependence on fossil fuels. Greater use of renewable energies like solar and wind will help us move in the right direction, but renewable energies are certain to add further complexity to our electric grid which is already showing signs of strain.
The use of renewable energy decentralizes the production of energy and means that the grid needs not only to provide electricity, but it also needs to be able to accept incoming power generated by renewables. This means to run smoothly, the electric grid needs to be able to predict both supply and demand.
Marc Spieler, energy sector technology executive at Nvidia, told EE Times that “the ability to add AI into the mix and do real time analytics at the edge is going to be critical for increasing the amount of distributed energy resources that can come online.”
But the utility industry is underinvested and slow to adopt technology. Adoption of AI may be a long ways away. Electricity utilities are spending about .3 percent of revenues on R&D. Ian Dobson, a University of Wisconsin EECS professor, said that in terms of percentage of spending on research that “we’re beat out easily by the pet food manufacturers.”
Kevin Walsh, Principal at OSIsoft, said that “data management falls into ‘crawl, walk, and run’ categories, and most utilities are crawling in their use of data right now. AI for data management would be ‘running.'”
Matt Schnugg, GE Digital VP for Data and Analytics, told Utility Dive that “knowing when to use data analytics and when to use machine learning and AI are the fundamental questions utilities are asking. Continuing to use an approach that has been good enough for years has merit, but new tools and capabilities may justify turning to data scientists and cloud computing.”
Sean McEvoy, senior vice president for energy at Veritone told EE Times that “no human could properly control the grid. Continuous real-time modelling of this tsunami of data delivers the intelligence to constantly know how much energy every grid participant needs, and how much energy can be to delivered, at any given moment or in the near future. Not only can no human do this – even massive computer power alone is also not enough. It demands edge compute power combined with intelligent reinforcement and adaptation learning.”
