Here’s why AI will be crucial for future US electrical grid reliability

When most Americans think of the infrastructure projects the Biden administration is proposing in the American Jobs Plan, they think of concrete, steel, and labor. But what if the biggest predictor of the success of the infrastructure plan is not in the materials but in artificial intelligence (AI) and machine learning (ML)?

Electrek spoke with Monte Zweben, CEO of Splice Machine, a database company that helps utilities and industrial companies implement data, about how AI/ML technologies could determine whether the American Jobs Plan succeeds as the US transitions to clean energy.

How will artificial intelligence and machine learning play a role in the development of infrastructure projects included in the American Jobs Plan?

As one of the forthcoming technologies of the 21st century, the Biden administration is investing significantly in research and development of American AI and machine learning technologies. US leadership in emerging technologies is critical to ensure economic competitiveness and national security, and AI, along with its myriad applications, is one of the premier research areas of investment.

How could AI and machine learning enable utilities to be more resilient in the face of external challenges?

As US electrical grids currently stand, there is no way to store energy on the grid, no batteries to hold it. For this to be effective, supply and demand must be equally matched. If energy isn’t consumed, it goes to waste. The introduction of Big Data to the electric industry allows machine learning models to analyze and predict demand with high levels of accuracy and produce supply accordingly. By taking advantage of our ability to analyze data beyond manual comprehension, we can better move electricity along the grid to where it’s most needed. 

Can you give examples of how AI could improve the transition to clean energy and the upgrade of US electrical grids? 

While wind and solar intermittency has only started to stress the physical limits of some US electrical grids, requirements for future intermittent assets far exceed existing infrastructure capacity. Better predictive balancing, efficient energy storage, and better integration intelligence will be crucial for future grid reliability. 

There is no precedent for any human-run balancing at this level of complexity. Only real-time machine learning models that can ingest, store, and analyze petabytes of multi-domain data will enable grid and utility operators and their AI-based advisory systems to keep up with rapidly changing multi-domain data.  

Do you think the Biden administration has adequately planned for the incorporation of AI into the American Jobs Plan to ensure success and maximum efficiency?

We are hopeful that the Biden administration has adequately planned for the incorporation of AI in the American Jobs Plan. By investing money into research and development in an equitable fashion, questions of bias and fairness in AI will be better answered, and innovation will be greater through increased diversity in the technology sector.

Monte Zweben is the CEO and co-founder of Splice Machine. A technology industry veteran, Monte’s early career was spent with the NASA Ames Research Center as the deputy chief of the artificial intelligence branch, where he won the prestigious Space Act Award for his work on the Space Shuttle program. He then founded and was the chairman and CEO of Red Pepper Software, a leading supply chain optimization company, which later merged with PeopleSoft, where he was VP and general manager, Manufacturing Business Unit. Then, Zweben was the founder and CEO of Blue Martini Software, the leader in e-commerce and omni-channel marketing. Monte is also the co-author of Intelligent Scheduling, and has published articles in the Harvard Business Review and various computer science journals and conference proceedings. He was Chairman of Rocket Fuel Inc. and serves on the Dean’s Advisory Board for Carnegie Mellon University’s School of Computer Science.