Time Series Clustering for Monitoring Fueling Infrastructure Performance with Kalai Ramea - #300
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Clustering Fueling Stations with Machine Learning
This chapter explores the use of machine learning to analyze spatiotemporal data for fueling infrastructure. It details a temporal clustering approach that categorizes fueling stations into distinct performance groups based on their consumption patterns, from healthy to overstressed. The implications of these findings for station reliability, regulatory interactions, and strategic placement of new stations for zero-emission vehicle adoption are thoroughly examined.
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