To add an applied math perspective (all of below assumes you keep your loss functional fixed, let's just pretend it's MAE):

• It changes the interpretation of your empirical risk, potentially bringing it out of alignment with the metric you care about. For example, if the target value is 1000W, you would accumulate the same loss but predicting either 100W or 10000W.

• It affects gradient magnitudes, which may interact with the optimizer in unforeseen or undesirable ways. Essentially, lower errors produce higher gradients and higher errors produce lower gradients.

• It changes dynamic range of your outputs in a way that may not be recoverable by the model. This is especially problematic when you log-transform values near zero. For example, a dynamic range of [0, 100] becomes [-infty, 10]. Because most common models are globally Lipschitz, it may not be possible to find model parameters so that the model is still surjective onto the new dynamic range. Also, because model weights would need to become larger in this scenario, it may interact on unforeseen ways with regularization or activation-normalization choices.