Electricity demand is influenced very strongly by holidays, strongly by weather and from weak to strong by geopolitics (depending on location).

The model will have a library of patterns, and will be able to pattern match subtle ones to deduce "this time series has the kind of micro-patterns which appear in strongly weather influenced time-series", and use this to activate the weather pattern cluster.

To use your example, when served thermometer data, the model notices that the holiday pattern cluster doesn't activate/match at all, and will ignore it.

And then it makes sense to train it on the widest possible time series, so it can build a vast library of patterns and find correlations of activation between them.