Dr. Sukanta Basu
Delft University of Technology
I’ve been doing renewable energy related research for last almost 20 years. I’ve also had experience with machine learning going back to 1999, but for the last two years I’ve spent a lot of time understanding the new deep learning tools that could really help my research. This hackathon was a challenging one, full of excitement (and some frustration) – I had my ups and downs working on the solution. But if you know the domain reasonably well, I think machine learning can do miracles for you. I guess all these years spent studying atmospheric physics actually paid off.
Ricardo Lara, PhD candidate in petroleum engineering at The University of Texas at Austin (left)
Roderick Perez, physical engineer, Data Science student at University of Vienna (right)
Ricardo: This hackathon has been a great networking experience. Me and Roderick never met in person. We connected in social media and decided to form a team. It was stimulating to discuss our ideas and get feedback from Shell and NVIDIA mentors, get inspired by these conversations and put new ideas into action.
Roderick: Both of us have background in oil-and-gas engineering and we are both passionate about access to energy. For me, it was an opportunity to step into the world of renewable energy and prove to myself I can be a part of a solution to the world’s current climate challenges.
Akshat Gupta, data scientist (left)
Sumit Yadav, data scientist (right)
Sumit: We’ve been working together in similar competitions for the past two years and got very intrigued by this challenge, as we realise forecasting energy production in solar farms and its impact on supply and demand is a valid problem in the energy space.
We are motivated by a vision of making a real change, so we are thinking about publishing a paper and releasing our source code so the whole community can benefit from our solution.
Akshat: We loved the competitive nature of this hackathon. Monitoring the leaderboard and continuously improving the accuracy of our algorithm to get ahead of other teams kept us going. But it was about more than good scores. We paid extra attention to our model being scalable and deployable, so real people can use the technology to make their lives better.