At their core, machine learning tools capture lots of complex information, learn from it, then apply what they learn to better estimate unknowns and predict future events. As the keepers of enormous datasets that defy conventional analysis, utilities could benefit from machine learning in a big way. Here are seven fundamental business challenges it could help them solve.
As of today, we’ve collected 400 billion meter reads of electric and gas consumption. Our technology platform is now analyzing 40 percent of all residential energy data generated in the United States. And our data warehouse as a whole, which spans four continents, remains one of the largest in the world.
As a manager at Baltimore Gas and Electric, she's responsible for designing, implementing, and operating one of the world's largest peak-time rebate programs. And she's found incredible success: in just two summers, her program has helped BGE's customers save more than $13 million on their energy bills.
Dr. Robert Cialdini is one of the world's leading experts in doing exactly that. He's the man behind Influence, a classic text from psychology and business that famously laid out six behavioral science principles behind successful marketing and outreach campaigns.
We cracked open Opower’s energy data storehouse (the world’s largest, spanning more than 50 million households worldwide). We analyzed hourly electricity data from 25,000 solar homes in the western US, alongside public data about 110,000 residential solar projects installed in California since 2007.
Leading utilities want to deliver that experience. All they need is a strategy to break through the noise, and reach SMEs with the energy management information they need to keep growing the bottom line.