Data-Driven Uncertainty Set Partitions for Robust and Adaptive Energy Forecasting with Missing Data

This is a smarter way to forecast energy demand or production that explicitly plans for bad data and surprises. Think of it as a weather forecast for the power grid that not only predicts the most likely outcome, but also builds safety margins that automatically adjust when sensors fail or data goes missing.