This article is part of ISEN's series highlighting alumni in sustainability and energy. MORE PROFILES
Improving electrical efficiency with predictive models
While in graduate school, Kim Montgomery was not planning a career in the utility industry, but she says her academic background in applied math positioned her to excel in her current role—building predictive models to allow utility companies to improve electrical efficiency. Montgomery graduated from Northwestern University with a master’s degree in 2002 and a PhD in 2004—both in engineering sciences and applied mathematics. While she initially studied applications for differential equations in the field of biology, Montgomery later decided to work in the energy field for a company called GridCure. This role became a source of pride for her as it has allowed her to learn and work across several fields.
GridCure, a Canadian company with offices in Boulder, Colorado, is a startup that provides predictive modeling advice to various utilities. The company has clients across the globe from the American Midwest to Portugal. As Head of Analytics, Montgomery works closely with engineers and uses smart meter and other sensor data to develop analytical software.
“In the last decade, smart meters have become more common, which means utilities are getting more data than they’ve ever dealt with before,” says Montgomery. “GridCure helps utilities with the analysis of smart meter data to predict the amount of electricity that different houses will use.”
Smart meters automatically track and communicate a customer’s electricity usage by the minute, providing more granular data than older manually-read meters. This means there is more fine-grained information available on how and when people use energy.
“If utilities have a more precise idea of how much energy is going to be used, and at what times of day, they can increase efficiency by more accurately planning how much energy to purchase on behalf of their customers,” explains Montgomery. Predictive modeling can also be applied to predict which network equipment needs to be replaced to improve efficiency and avoid catastrophic failures.
Although Montgomery notes that she was not studying electrical engineering or specifically focusing on the energy industry while at Northwestern, she is confident her education prepared her for her current career. According to Montgomery, energy data analysis is an interdisciplinary field requiring her to draw on her knowledge of applied math, chemistry, and engineering to analyze data such as transformer failure data.
“As early as possible, it’s important to develop an in-depth technical experience and find opportunities to work with real data,” says Montgomery. “My experience came from the applied math department at Northwestern, Kaggle competitions, and working with people with experience in various fields.”
As for the future, Montgomery plans to continue working with GridCure. Upcoming projects may include determining the most efficient placement of electric vehicle charging stations. These stations can consume a large quantity of energy, thus it is important to place them in a location where they will be used the most and where the network can handle the stress of additional power demand.
“I am proud of the mathematical modeling experience that I can provide, but I think many of my biggest accomplishments are still ahead of me,” concludes Montgomery. “As the amount of data available in the utility industry proliferates in the next couple of years, we will be able to contribute models that really improve the utility industry’s day-to-day performance.”
This article is part of ISEN's series highlighting alumni in sustainability and energy. MORE PROFILES
