Eneryield funding news – Energy Tech Startup Eneryield Secures Undisclosed Amount of Seed Funding

ABB and Chalmers Ventures supplied funding, and the business had already raised $120,000. Eneryield, which was founded in 2019 and emerged from research at Chalmers University of Technology, provides grid maintenance by lowering unscheduled outages through machine learning incorporated into current systems.

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Without requiring extra gear like sensors, its Intelliview software fits into power networks that are already in place. Currently, Intelliview can be deployed on-premises or in the cloud for commercial projects and pilot programmes.

CEO Dr. Ebrahim Balouji commented on the raise: “With support from strategic investors like ABB and Chalmers Ventures, we are ready to take our solution to the market. Seeing our research fields combined and come to life as a commercial product is motivating to say the least. Our timing is perfect with the growing demand for green energy and the world's economic and security situation; the need for local, smart energy grid solutions is growing rapidly, and we are ready to meet that demand.”

Sami Raitakoski, Grid Components Global Product Group Manager at ABB, said: "The portfolio synergies between ABB and Eneryield will help create one of the most advanced prediction systems for power networks. Combining Eneryield’s AI analytics with ABB relays and centralized or virtualized protection and control systems will result in improved accuracy and better maintenance planning for utility and industry customers. The early warning gives the grid operator the time needed to take necessary actions to avoid faults and outages, resulting in better grid reliability and reduced operational costs."

About Eneryield

Swedish startup Eneryield specialises in creating cutting-edge software powered by machine learning that is specifically intended to predict and assess early-stage defects. The Chalmers University of Technology spent several years researching before producing Intelivew, one of their main products. By using sophisticated algorithms to predict and analyse any problems, the product helps prevent disruptions through proactive component maintenance.