Submission · anonymous
This paper presents model-based and data-driven HVAC control strategies for residential demand response. A first-order RC thermal model captures building thermal dynamics. Model predictive control (MPC) optimizes HVAC scheduling to minimize energy cost while satisfying thermal comfort constraints. A data-driven rule-based strategy provides a computationally lighter alternative. Both strategies are evaluated under time-of-use pricing and direct load control scenarios.