Control of HVAC systems via scenario-based explicit MPC

Abstract

Abstract-Improving energy efficiency of Heating, Ventilation and Air Conditioning (HVAC) systems is a primary objective for the society. Model Predictive Control (MPC) techniques for HVAC systems have recently received particular attention, since they can naturally account for several factors, such as weather and occupancy forecasts, comfort ranges and actuation constraints. Developing effective MPC based control strategies for HVAC systems is nontrivial, since buildings dynamics are nonlinear and affected by various uncertainties. Further, the complexity of the MPC problem and the burden of on-line computations can lead to difficulties in integrating this scheme into a building management system.We propose to address this computational issue by designing a scenario-based explicit MPC strategy, i.e., a controller that is simultaneously based on explicit representations of the MPC feedback law and accounts for uncertainties in the occupancy patterns and weather conditions by using the scenarios paradigm. The main advantages of this approach are the absence of a-priori assumptions on the distributions of the uncertain variables, the applicability to any type of building, and the limited on-line computational burden, enabling practical implementations on low-cost hardware platforms.We illustrate the practical implementation of the proposed explicit MPC controller on a room of a university building, showing its effectiveness and computational tractability. I. INTRODUCTIONHeating, Ventilation and Air Conditioning (HVAC) systems play a fundamental role in maintaining acceptable indoor comfort levels; reports indicate that HVAC systems in developed countries contribute for approximately one fifth of the total national energy usages [1]. Current practice shows its limits, with potential energy savings achievable by using systematic building management being estimated from 5% to 30% of the total consumptions [2], [3]. An effective controller for HVAC systems should incorporate time-dependent energy costs, bounds on the control actions, comfort requirements, as well as account for system uncertainties, e.g., weather conditions and occupancy. A natural scheme that achieves the systematic integration of all the aforementioned elements is the Model Predictive Control (MPC) [4].Simulations in [5], [6], as well as the experimental results on real buildings reported in [7], [8], [9], show that MPC schemes can yield better comfort levels and energy use performance than current practices.

Stochastic Battery Operations using Deep Neural Networks

“…Scenario approach has been introduced into optimization problems to replace the uncertainty set of variables by a finite number of scenarios. In [11], [20] different approaches have been proposed to formulate the uncertainty set as scenarios either in objectives or constraints. Suppose we generate a set of K i.…”

Use of Model Predictive Control for Short-Term Operating Reserve Using Commercial Buildings in the United Kingdom Context

“…• more advanced Heating, Ventilation and Air Conditioning systems (HVACs), which allow power to be varied continuously compared to switched control in residential buildings [6]; • Building management systems (BMS), enabling the implementation of advanced control algorithms [7]. Additionally, the large thermal mass of CBs could be used as a virtual storage, making them especially interesting in a smart grid framework [8], [9].…”

Building model identification during regular operation - empirical results and challenges

“…where c p is the specific heat capacity of air, B i is the set of VAV boxes serving the i-th room and u j is the j-th element of u. Note that due to the lack of temperature measurements of the supply air at the outlet of VAV boxes, v Ts is the supply air temperature upstream of the VAV boxes' heating coils, i.e., heat gains due to reheating at the VAV boxes are not modeled by (2), but are captured by the internal gains EHFM in our model. Internal Gains: If the i-th BE is a room, then it is also subject to internal gains, which are modeled as:…”

“…Energy consumption of HVAC systems can be partly shifted in time without compromising occupant comfort, because of buildings' inherent thermal capacity. As a result, there has been extensive research, using frameworks such as model predictive control (MPC), trying to harness this intertemporal consumption flexibility and minimize energy usage of buildings [2], [3]. More recently, the feasibility of using buildings to provide ancillary services, such as frequency regulation, to the power grid has also been studied [4], [5], [6], [7], [8].…”