Who Will Shift? Learning Household Flexibility from Behavioral Patterns in Electricity Usage
This thesis develops an application-oriented framework to identify and predict residential demand-side flexibility from the temporal structure of electricity use, rather than energy totals. We construct structure-based, price-aligned labels that track shifts between peak and off-peak hours, and train three complementary models: a next-day behavioral classifier (M1) for short-horizon operations, a
