Utility-Gated Personalized Federated Forecasting for Peak-Constrained Building Energy Management
Year: 2026
Type: preprint
Abstract: Distributed building energy management depends on forecasts, yet federated forecasting is commonly evaluated without testing the decisions that those forecasts support. This study examines whether selective, communication-efficient collaboration improves both load prediction and battery operation. The proposed UtilityGossipmethod maintains separate private and collaborative low-rank adapters at each node. A peer adapter is incorporated only when randomized cross-fitting on local held-out data indicates a meaningful reduction in forecast error. Evaluation on public electricity and solar benchmarks covers longer horizons, alternative network topologies, node failures, and transmit-time noise. At the 96-step horizon, the method reduces Solar-Energy mean squared error by 1.59% relative to local training and avoids the greater than 3% degradation caused by unconditional federation on Electricity, using half the communication of federated averaging. Operational value is assessed by replaying every forecast source through the same frozen battery policy for a held-out week. An external projection prevents coordinated charging from increasing the running no-storage district peak. The proposed forecast supports a 19.34% cost reduction relative to no storage, and diagnostic replays confirm that forecast timing affects dispatch. Its additional control gain over a strong local forecast is not statistically resolved. The results support selective federation for distributed forecasting but show that lower prediction error does not necessarily yield proportional energy savings.
Source: Research Square
Author: Rui Wang
Institution: Microsoft (Canada)
Language: English
Topic: Smart Grid Energy Management
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225104705