# Building Energy Management

Distributed building energy management depends on forecasts, yet federated forecasting is usually scored on error without testing the decisions those forecasts support. UtilityGossip keeps a private and a collaborative low-rank adapter at each node and accepts a peer adapter only when randomized four-fold cross-fitting on local held-out data shows a meaningful error reduction. On Electricity, unconditional federation raised mean squared error more than 3% above local training while the gated method avoided that degradation; on Solar-Energy at the 96-step horizon it lowered mean squared error by 1.59% relative to local training using half the communication of federated averaging.

Distributed building energy management depends on forecasts, yet federated forecasting is usually scored on error without testing the decisions those forecasts support. UtilityGossip keeps a private and a collaborative low-rank adapter at each node and accepts a peer adapter only when randomized four-fold cross-fitting on local held-out data shows a meaningful error reduction. On Electricity, unconditional federation raised mean squared error more than 3% above local training while the gated method avoided that degradation; on Solar-Energy at the 96-step horizon it lowered mean squared error by 1.59% relative to local training using half the communication of federated averaging.