Efficient Data-Driven MPC for Demand Response of Commercial Buildings

• URL: http://arxiv.org/abs/2401.15742v2
• Date: Thu, 16 May 2024 01:11:20 GMT
• Title: Efficient Data-Driven MPC for Demand Response of Commercial Buildings
• Authors: Marie-Christine Paré, Vasken Dermardiros, Antoine Lesage-Landry,
• Abstract summary: We propose a data-driven and mixed-integer bidding strategy for energy management in small commercial buildings. We consider rooftop unit heating, air conditioning systems with discrete controls to accurately model the operation of most commercial buildings. We apply our approach in several demand response (DR) settings, including a time-of-use, and a critical rebate bidding.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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