A “Smart Model-Then-Control” Strategy for the Scheduling of Thermostatically Controlled Loads

Abstract

Model predictive control (MPC) has been widely adopted for indoor temperature control and building energy management. There are two steps in traditional MPC: 1) modeling thermal dynamics as the state space function to represent the temperature variation influenced by thermostatically controlled loads (TCLs); 2) formulating an optimization problem for optimal scheduling of TCLs within the control horizon. However, such a “model-then-control” strategy could result in biased control because of the unaligned modeling error and control cost, i.e., minimization of model errors may not necessarily lead to minimal costs against actual thermal dynamics in buildings. To tackle this problem, we advocate for a “smart model-then-control” (SMC) strategy that incorporates thermal dynamics modeling into the temperature control task. In particular, instead of using mean squared errors (MSE), we adopt the control objective as the task-specific loss function to guide the model training. We further formulate an Input Convex Neural Network (ICNN)-based surrogate loss function, which is differentiable and convex for effective training. In this way, the objectives of both model training and temperature control in MPC are well-aligned to obtain cost-effective decisions. We validate the performance of the SMC strategy in single-zone and multi-zone buildings. The simulation results show that it can reduce control costs by 5.97% and 2.10% respectively when compared with traditional MPC.