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07: Spatial Clustering for district energy
A big problem especially for local governments today is identifying regions of cities where district heating could be applied. In the past they used simple methods based on building densities and sizes. Machine learning can be used to improve on this.
Thanks to our models we know a lot about cities and districts. We are looking for novel ways to use machine learning clustering methods to generate clusters of buildings that are close together and share important characteristics for connecting to district heating.
Challenge Owner: Planeto Energy/University Geneva Jonathan Chambers, Stefano Cozza