# Load Forecasting

ElecLoad reads weather alerts, grid notices and news event descriptions alongside historical load, judging each event's relevance to the target load, the timing of its effect, and whether events reinforce or offset one another, then decides whether to forecast the whole horizon at once or split it where event effects change. On the segmented path, sub-agents draft each segment independently and the main agent reviews them and sends feedback to selected segments only. Across hourly load data including GEFCom2014, PJM and LCL it ranks near the top in most evaluation scenarios; under multiple events on PJM its WAPE is 3.69% versus 4.31% for a direct-only variant using the same model and inputs.

ElecLoad reads weather alerts, grid notices and news event descriptions alongside historical load, judging each event's relevance to the target load, the timing of its effect, and whether events reinforce or offset one another, then decides whether to forecast the whole horizon at once or split it where event effects change. On the segmented path, sub-agents draft each segment independently and the main agent reviews them and sends feedback to selected segments only. Across hourly load data including GEFCom2014, PJM and LCL it ranks near the top in most evaluation scenarios; under multiple events on PJM its WAPE is 3.69% versus 4.31% for a direct-only variant using the same model and inputs.

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.