Renewable = Reliable
We use machine learning for pre-emptive scheduling
for maintenance of wind turbine fleets
Zog
Data-driven innovation in offshore wind
with the growing demand by operators in a lean and cost-effective approach to O&M planning
Many original manufacturers’ warranties now reaching expiry, the target market expands from new wind farms to existing wind farms as well. Zog.Wind is a cloud-based decision-support system that keeps on learning from in-farm and environment data. The output empowers users to make the best use of the vessels, crew and time.
Our journey
We have set out with a passion to create an impact for a sustainable tomorrow.
It has been a steep learning curve for us Zoggers since the inception. We had many experiences and interactions so far, both critical and encouraging, which has helped us re-iterate on our solution and more importantly update our business methods. We have reached good traction and are well on track with developing the MVP. Pilot tests are on the way.
Meet the team
In four words. We are creative, professional, approachable and forward-looking.
Sriram Venkatakrishnan
CEO. Data Scientist
MSc EIT-KIC Energy for Smart Cities (KTH, Sweden; KU Leuven, Belgium)
BSc Electrical and Electronics Engineering (Anna University, India)
Data Science Intern, Power2U Sweden AB.
Seyedeh Armaghan Bahreini
Energy Analyst
MSc EIT-KIC Energy for Smart Cities (KU Leuven, Belgium; KTH, Sweden)
BSc Mechanical Engineering(Shiraz university, Iran)
Master thesis candidate, ABB/Belgium
Evgeny Genov
Data Scientist, Brand Executive
MSc EIT-KIC Energy for Smart Cities (KTH, Sweden; KU Leuven, Belgium)
BSc Physics and Mathematics (UCU, Utrecht University).
Researcher, Energyville/KTH
Tech journalist, Toshi Times
Steffy Ann Thomas
Business Analyst
MSc EIT-KIC Energy for Smart Cities (KTH, Sweden; INP Grenoble, France)
B.Tech Electrical and Electronics Engineering (KIT, India)
Planning engineer - Galfar Almisnad Eng. & Contracting WL.L. , Doha-Qatar
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