A Different Forecast Without a Different Model
TL;DR for operators A utility forecasting hundreds of heterogeneous customers and distribution transformers faces a model-management decision: maintain one shared forecaster, train separate local models, or specialize a common model without multiplying its full parameter set. Li, Cheng, and Weng demonstrate a third architecture. Their method keeps a probabilistic forecasting backbone fixed and constructs customer-specific predictive mean and variance heads from a shared bank of low-rank adaptations. Customers can combine several adapters according to their load characteristics rather than receiving an independently trained model. ...