What inspired you to pursue a career in electrical power engineering?
In high school, I was sporty and good at math, and power engineering was just one of many options I considered after graduation. I decided to go with it. I immersed myself in the field during my bachelor’s thesis and became deeply motivated in my master’s studies. That’s when I felt I genuinely chose electrical power engineering. Before that, it was more about pursuing something that came naturally to me and was educational.
Can you share some highlights from your time at Elering? How did those experiences shape your professional path?
I joined Elering straight out of university as a complete rookie with no practical knowledge of power systems. I’m grateful I started in the power lines operation and maintenance unit, working alongside some of Estonia's best power line experts. This experience gave me a solid understanding of how transmission lines function, both generally and in detail.
While pursuing my PhD on condition assessment and asset management of transmission lines, I had the unique opportunity to implement novel methodologies and collect real data from the Estonian grid with the support of the maintenance department. This collaboration allowed me to test concepts previously thought impossible and gain valuable feedback directly from experts. It was a win-win: I developed methodologies that transformed asset management at Elering, many of which are still used today.
Share some academic highlights.
My academic achievements are closely tied to my PhD studies. I defended my thesis in March 2022, titled “Data-Driven Asset Management and Condition Assessment of Transmission Overhead Lines.” During this time, I published academic papers and presented my research at various conferences (more details can be found on my ETIS page).
As a fun fact, I was a visiting researcher at the University of the West Indies in Trinidad and Tobago. There, I collaborated with amazing researchers combining electrical power engineering with novel machine learning approaches. I continue to stay active in academia, lecturing on power systems and power grids, which helps keep my knowledge sharp and allows me to share it with students.
What inspired you to co-found Gridraven?
While working at Elering and conducting research during my PhD, I saw how conservative and inefficient the energy sector can be. There’s a saying among engineers: “The physics of electricity hasn’t changed in 60 years.” That’s true, but advancements in technology, such as machine learning, now allow us to implement methodologies that were once impossible or economically unfeasible.
Dynamic Line Rating (DLR) is a great example. While the concept has existed since 1958, technology has only recently advanced enough to make it practical and impactful.
How has your expertise contributed to your work at Gridraven?
My seven years in the Estonian transmission system gave me a deep understanding of how power systems, particularly transmission lines, work in practice. My PhD research, which combined theoretical and interdisciplinary approaches, taught me the value of integrating diverse fields to solve complex problems. This unique blend of practical experience and academic research has laid the foundation for developing innovative solutions at GridRaven.
What excites you most about working at the intersection of technology, machine learning, and energy systems?
No one is an expert in everything, and solving interdisciplinary problems requires collaboration with specialists in diverse fields. Working with brilliant people to tackle complex challenges makes the journey incredibly exciting and educational.
What specific problems are you solving at Gridraven, and why are they important for the energy industry?
We’re addressing the limited capacity of existing transmission lines. Transmission lines are the backbone of energy systems, and upgrading them to accommodate new generation or consumption units is expensive and time-consuming.
Our solution increases the capacity of overhead lines by up to 30% annually—sometimes as much as 50% during windy periods—without requiring new infrastructure. This can significantly alleviate bottlenecks, reduce costs, and expedite grid expansion efforts.
Can you walk us through a recent project you’ve worked on? What were the main challenges and outcomes?
We’re currently piloting our solution with Elering, covering 5,500 km of transmission lines. This project involves validating predictions of line ratings based on weather and temperature data.
Our initial results are promising, showing we can predict line ratings up to 48 hours in advance with predetermined confidence intervals. Over a two-month period, we achieved an average capacity increase of nearly 40% compared to traditional approaches, using a 95% confidence level.
The biggest challenge has been managing the sheer volume of data—around 15,000 line rating predictions per hour across the Estonian grid. However, seeing the results align with measurements and exceed expectations has made the effort worthwhile.
Can you share an example of a technical breakthrough or innovative approach you’ve introduced at Gridraven?
Our biggest breakthrough is determining dynamic line ratings without relying on hardware. Most competitors use sensors on high-voltage lines, but our approach relies purely on data. By accurately predicting cooling and heating inputs, we can estimate line ratings effectively, even accounting for random variables like weather conditions.
What’s one surprising insight or result you’ve uncovered in your work so far?
We’ve demonstrated the ability to predict safe, operable line ratings several days in advance with confidence intervals that hold true. This opens up significant opportunities for the day-ahead electricity market, where our predictions could reduce prices and improve grid efficiency.
How do you see the energy sector evolving with the integration of machine learning and advanced technology?
The energy sector will always be conservative due to its critical role in society. However, utilities are increasingly open to adopting AI and ML as they recognize the potential benefits. The key is ensuring these technologies are rigorously tested and proven before implementation.
What role does Gridraven play in this transformation, and what makes its solutions unique?
Our solution leverages advancements in technology and machine learning to unlock the full potential of existing infrastructure. Unlike traditional methods that require costly hardware or supercomputers, we deliver results efficiently and reliably using state-of-the-art algorithms.
What potential long-term impact do you hope your work will have on energy systems and sustainability?
Our goal is to maximize the capacity and safety of overhead lines, improving the efficiency of existing infrastructure and reducing bottlenecks in electricity markets. This can lower energy costs and support the transition to more sustainable energy systems.
What’s one thing about your work at Gridraven that makes you proud?
Building an incredible team and turning an ambitious idea into something meaningful and impactful.
What message would you like to share about the importance of innovation in the energy sector?
Power systems are largely built on decades-old principles. With digitalization and advancements in machine learning, we now have the tools to implement solutions that were once purely theoretical. While we shouldn’t rush to apply AI everywhere, it’s essential to carefully explore its capabilities and potential.
