Brandon Hencey

Model Predictive Control for the Operation of Building Cooling Systems

This brief presents a model-based predictive control (MPC) approach to building cooling systems with thermal energy storage. We focus on buildings equipped with a water tank used for actively storing cold water produced by a series of chillers. First, simplified models of chillers, cooling towers, thermal storage tanks, and buildings are developed and validated for the purpose of model-based control design. Then an MPC for the chilling system operation is proposed to optimally store the thermal energy in the tank by using predictive knowledge of building loads and weather conditions. This brief addresses real-time implementation and feasibility issues of the MPC scheme by using a simplified hybrid model of the system, a periodic robust invariant set as terminal constraints, and a moving window blocking strategy. The controller is experimentally validated at the University of California, Merced. The experiments show a reduction in the central plant electricity cost and an improvement of its efficiency.

Model Predictive Control of thermal energy storage in building cooling systems

A preliminary study on the control of thermal energy storage in building cooling systems is presented. We focus on buildings equipped with a water tank used for actively storing cold water produced by a series of chillers. Typically the chillers are operated each night to recharge the storage tank in order to meet the buildings demand on the following day. A Model Predictive Control (MPC) for the chillers operation is designed in order to optimally store the thermal energy in the tank by using predictive knowledge of building loads and weather conditions. This paper addresses real-time implementation and feasibility issues of the MPC scheme by using a (1) simplified hybrid model of the system, (2) periodic robust invariant sets as terminal constraints and (3) a moving window blocking strategy.

Model predictive control for the operation of building cooling systems

A model-based predictive control (MPC) is designed for optimal thermal energy storage in building cooling systems. We focus on buildings equipped with a water tank used for actively storing cold water produced by a series of chillers. Typically the chillers are operated at night to recharge the storage tank in order to meet the building demands on the following day. In this paper, we build on our previous work, improve the building load model, and present experimental results. The experiments show that MPC can achieve reduction in the central plant electricity cost and improvement of its efficiency.

A Robust Controller Interpolation Design Technique

Switching or blending among controllers is termed controller interpolation. This paper investigates a robust controller interpolation technique and applies it to an experimental test bed. Although an interpolated controller is composed of linear time-invariant (LTI) controllers stabilizing the LTI plant, closed-loop performance and stability are not guaranteed. Thus, it is of interest to design the interpolated controller to guarantee closed-loop stability and a performance level for all interpolation signals describing controller switching sequences and combinations. The performance metric that is under investigation in this paper is the H ¿ norm. A suboptimal robust interpolated-controller design algorithm is framed in terms of bilinear matrix inequalities. The motivating example demonstrates the efficacy of the robust interpolated-controller design.

Model predictive HVAC control with online occupancy model

Abstract This paper presents an occupancy-predicting control algorithm for heating, ventilation, and air conditioning (HVAC) systems in buildings. It incorporates the buildings thermal properties, local weather predictions, and a self-tuning stochastic occupancy model to reduce energy consumption while maintaining occupant comfort. Contrasting with existing approaches, the occupancy model requires no manual training and adapts to changes in occupancy patterns during operation. A prediction-weighted cost function provides conditioning of thermal zones before occupancy begins and reduces system output before occupancy ends. Simulation results with real-world occupancy data demonstrate the algorithms effectiveness.

Decentralized Feedback Structures of a Vapor Compression Cycle System

In vapor compression cycle systems, it is desirable to effectively control the thermodynamic cycle by controlling the thermodynamic states of the refrigerant. By controlling the thermodynamic states with an inner loop, supervisory algorithms can manage critical functions and objectives such as maintaining superheat and maximizing the coefficient of performance. In practice, it is generally preferred to tune multiple single-input-single-output (SISO) control inner loops rather than a single multiple-input-multiple-output control inner loop. This paper presents a process by which a simplified feedback control structure, amenable to a decoupled SISO control loop design, may be identified. In particular, the many possible candidate input-output (I/O) pairs for decentralized control are sorted via a decoupling metric, called the relative gain array number. From a reduced set of promising candidate I/O pairs, engineering insight is applied to arrive at the most effective pairings successfully verified on an experimental air-conditioning-and-refrigeration test stand.

Online thermal estimation, control, and self-excitation of buildings

This paper investigates a method to improve building control performance via online identification and excitation (active learning process) that does not disrupt normal operations. In previous studies we have demonstrated scalable methods to acquire multi-zone thermal models of passive buildings using a gray-box approach that leverages building topology and measurement data. Here we extend the method to multi-zone actively controlled buildings and examine how to improve the thermal model estimation by using the controller to excite unknown portions of the building dynamics. Comparing against a baseline thermostat controller, we demonstrate the utility of both the initially acquired and improved models with a Model Predictive Control (MPC) framework, which includes weather uncertainty and time-varying temperature set-points. By coupling building topology, estimation, and control routines into a single online framework, we have demonstrated the potential for low-cost scalable methods to actively learn and control buildings for optimal occupant comfort and minimum energy usage, all while using the existing buildings HVAC sensors and hardware.

A KYP Lemma for LMI Regions

In this technical note, a Kalman-Yakubovich-Popov (KYP) lemma is discussed for linear matrix inequality (LMI) regions. Sufficient quadratic stability conditions are developed for an uncertain linear system subject to time varying uncertainty satisfying a quadratic inequality. Furthermore, the quadratic stability conditions are shown to guarantee the satisfaction of a frequency domain inequality.

Predictive HVAC control using a Markov occupancy model

This paper presents a model predictive control (MPC) technique for building heating, ventilation, and air conditioning (HVAC) systems. It incorporates the buildings thermal dynamics, local weather predictions, and a stochastic occupancy model to reduce energy consumption while maintaining occupant comfort. Using approximate dynamic programming and a cost function weighted by expected occupancy, the scheme extends the capability of conventional model predictive control by pre-conditioning thermal zones before occupancy begins and reducing conditioning before occupancy ends. The resulting control law may be synthesized step-wise using an on-line optimization or may be periodically synthesized off-line and downloaded into an embedded controller. Simulation results demonstrate the efficacy of both approaches.

Energy-comfort optimization using discomfort history and probabilistic occupancy prediction

Heating ventilation and air-conditioning (HVAC) systems consume a significant portion of the energy within buildings. Current HVAC control systems use simple fixed occupant schedules, while proposed energy optimization schemes do not consider past discomfort in making future energy optimization decisions. We propose a Model-based predictive control (MPC) algorithm that adaptively balances energy and comfort while the system is in operation. The algorithm combines occupancy prediction with the history of occupant discomfort to constrain expected discomfort to an allowed budget. Our approach saves energy by dynamically shifting discomfort over time based on its real time performance. The system adapts its behavior according to the past discomfort and thus plays the dual role of saving energy when discomfort is smaller than the target budget, and maintaining comfort when the discomfort margin is small. Simulation results using synthetic benchmarks and occupancy traces demonstrate considerable energy savings over a smart reactive approach while meeting occupant comfort objectives.