Efficient Data-Driven MPC for Demand Response of Commercial Buildings
Marie-Christine Paré Vasken Dermardiros Antoine Lesage-Landry
Model predictive control (MPC) has been shown to significantly improve the
energy efficiency of buildings while maintaining thermal comfort. Data-driven
approaches based on neural networks have been proposed to facilitate system
modelling. However, such approaches are generally nonconvex and result in
computationally intractable optimization problems. In this work, we design a
readily implementable energy management method for small commercial buildings.
We then leverage our approach to formulate a real-time demand bidding strategy.
We propose a data-driven and mixed-integer convex MPC which is solved via
derivative-free optimization given a limited computational time of 5 minutes to
respect operational constraints. We consider rooftop unit heating, ventilation,
and air conditioning systems with discrete controls to accurately model the
operation of most commercial buildings. Our approach uses an input convex
recurrent neural network to model the thermal dynamics. We apply our approach
in several demand response (DR) settings, including a demand bidding, a
time-of-use, and a critical peak rebate program. Controller performance is
evaluated on a state-of-the-art building simulation. The proposed approach
improves thermal comfort while reducing energy consumption and cost through DR
participation, when compared to other data-driven approaches or a set-point
controller.