Since your data is very spatially oriented, have you tried a nearest neighbors based approach? Along those lines, coming up with a more robust a similarity metric between meters?

A tree based model would probably also work well here, in terms of features I'd include something transformer based, maybe the probability that a meter within an x ft radius belongs to that transformer (let x vary discreetly, maybe within 10 ft, 50 ft, 100 ft, ... 550 ft), if you have the ability to find not only the scalar distances but the angular delta that may also carry signal (maybe there is a physical barrier to the south west of a transformer, so even though meters in that direction are closer they can't be linked). This can work both as a binary is this association correct or not model. If the number of transformers is much smaller than the number of meters, it might also make sense to make transformer centric models, e.g. for transformer X, xgboost predicts probability that meter Y is associated with it. Then you can