Key Takeaways
– In smart grids, large amounts of historical data are available but rarely used for predictive maintenance. Mining and analyzing these historical data can provide valuable information.
– Applying joint-human machine learning and self-monitoring for detecting faults and errors in district heating using the concept of wisdom of the crowd
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Speaker Bio
Hassan Nemati has been working at Halmstad University and Halmstad Energi och Miljö during his doctoral education. His research topic during PhD studies was about data analytics for weak spots detection in smart distribution grids. He received his Lic. Tech on Sep 2017.
Stage 1 | Applied Data-Driven Maintenance
Program Day 2, Hassan Nemati and Kobra Etminani – Halmstad University