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Dr. Hakan Umit's picture
Decision-making Expert for Large Scale Systems using Quantitative Methods, PĂ´le Position

Self-starter technology development executive & management consultant with strong technical foundations in algorithm & software development. Digitalisation expert for a wide range of...

  • Member since 2021
  • 11 items added with 3,107 views
  • Mar 30, 2021
  • 478 views

📰 I present 3 renewables use cases for Vattenfall, Ørsted and #Britain, in which Machine Learning and Quantitative Optimization help

1️⃣ improve energy yield up to 10%,
2️⃣ predict (with 99% accuracy) the potential of new wind farm sites,
3️⃣ save millions of tons of CO2 per year, and
4️⃣ demonstrate how Britain's domestic transport demand can be met by renewables only.

Discussions
Matt Chester's picture
Matt Chester on Mar 30, 2021

The task of optimizing renewable energy integration have been around for several decades, yet many problems are still considered as mathematical challenges

This is such an important point that I think those outside of the world of AI, ML, and these digital tools don't fully understand. There's low-hanging fruit out there not being captured by the amazing physical technology we have-- and optimization can make all the difference in ROI and minimize supply/demand challenges (in the same way EE programs are meant to do)

Dr. Hakan Umit's picture
Dr. Hakan Umit on Mar 31, 2021

I think quantitative optimization and advanced ML algorithms must be part of cost-benefit assessments prior to large investments, as they are the most effective ways of truly demonstrating the potential of a new venture.

Dr. Hakan Umit's picture
Thank Dr. Hakan for the Post!
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