Yudong Ma

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.

Predictive Control for Energy Efficient Buildings with Thermal Storage: Modeling, Stimulation, and Experiments

The building sector is the largest energy consumer in the world. Therefore, it is economically, socially, and environmentally significant to reduce the energy consumption of buildings. Achieving substantial energy reduction in buildings may require rethinking the whole processes of design, construction, and operation of a building. This article focuses on the specific issue of advanced control system design for energy efficient buildings.

A distributed predictive control approach to building temperature regulation

We study the problem of temperature regulation in a network of building thermal zones. The control objective is to keep zone temperatures within a comfort range while consuming the least energy by using predictive knowledge of weather and occupancy. First, we present a simplified two-mass nonlinear system for modeling thermal zone dynamics. Model identification and validation based on historical measured data are presented. Second, a distributed model-based predictive control (DMPC) is designed for optimal heating and cooling. The DMPC is implemented by using sequential quadratic program and dual decomposition. Simulation results show good performance and computational tractability of the resulting scheme.

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.

Stochastic Model Predictive Control for Building HVAC Systems: Complexity and Conservatism

This paper presents a stochastic model predictive control (SMPC) approach to building heating, ventilation, and air conditioning (HVAC) systems. The building HVAC system is modeled as a network of thermal zones controlled by a central air handling unit and local variable air volume boxes. In the first part of this paper, simplified nonlinear models are presented for thermal zones and HVAC system components. The uncertain load forecast in each thermal zone is modeled by finitely supported probability density functions (pdfs). These pdfs are initialized using historical data and updated as new data becomes available. In the second part of this paper, we present a SMPC design that minimizes expected energy cost and bounds the probability of thermal comfort violations. SMPC uses predictive knowledge of uncertain loads in each zone during the design stage. The complexity of a commercial building requires special handling of system nonlinearities and chance constraints to enable real-time implementation, minimize energy cost, and guarantee thermal comfort. This paper focuses on the tradeoff between computational tractability and conservatism of the resulting SMPC scheme. The proposed SMPC scheme is compared with alternative SMPC designs, and the effectiveness of the proposed approach is demonstrated by simulation and experimental tests.

Analysis of local optima in predictive control for energy efficient buildings

We study the problem of heating, ventilation and air conditioning (HVAC) control in a typical commercial building. We propose a model predictive control (MPC) approach which minimizes energy cost while satisfying occupant comfort and control actuator constraints, using a simplified system model and incorporating predictions of future weather and occupancy inputs. In simplified physics-based models of HVAC systems, the product between air temperatures and flow rates arising from energy balance equations leads to a non-convex MPC problem. Fast computational techniques for solving non-convex optimization can only provide certificates of local optimality. Local optima can potentially cause MPC to have worse performance than existing control implementations, so deserve careful consideration. The objective of this article is to investigate the phenomenon of local optima in the MPC optimization problem for a simple HVAC system model. In the first part of the article, simplified physics-based models and MPC design for two common HVAC configurations are introduced. In the second part, simulation results exhibiting local optima for both configurations are presented. We perform a detailed analysis on the different types of local optima and their physical interpretation. We then use this analysis to derive physics-based rules to exclude classes of locally optimal control sequences under specific conditions.

Fast stochastic predictive control for building temperature regulation

This paper presents a nonlinear stochastic model predictive control (MPC) approach to building thermal temperature regulation. The control objective is to minimize energy consumption while bounding the probability of thermal comfort violations using prediction of weather and occupancy. We exploit the structure of the bilinear thermal network model and propose a partial closed-loop control scheme. This allows analytical computation of the predicted state variance matrices and easy reformulation of the stochastic MPC problem into a nonlinear program (NLP). We present a tailored sequential quadratic programming method to solve the NLP by exploiting its sparsity. Simulation results show good performance and computational tractability of the resulting scheme.

Fast stochastic MPC with optimal risk allocation applied to building control systems

This paper presents a method for solving linear stochastic model predictive control (SMPC) subject to joint chance constraints. The chance constraints are decoupled using Booles inequality and by introducing a set of unknowns representing allowable violation for each constraint (the risk). A tailored interior point method is proposed to explore the special structure of the resulting SMPC problem. The proposed method is compared with existing two-stage algorithms with the first stage allocating the risks and the second stage optimizing the feedback control gain. The approach is applied to building control problems that minimizes energy usage while keeping thermal comfort by making use of uncertain predictions of thermal loads and ambient temperature. Extensive numerical tests show the effectiveness of the proposed approach.

Distributed Model Predictive Control for Forced-Air Systems

This chapter focuses on advanced control design, specifically for forced air HVAC systems. Such advanced control schemes incorporate predictions of weather, occupancy, renewable energy availability, and energy price signals in order to deliver performance-driven automated decision making at a hierarchy of levels. The chapter covers thermal modeling for controls, predictive control design, and implementation of such controllers in real-world buildings. An overview of standard computational platforms and communication systems in buildings is reported. Our main objective is to discuss how advanced control relates to the existing building practices; in particular, a distributed control logic “Trim and Respond” is described in detail. The “Trim and Respond” logic is shown to match a one-step explicit distributed model predictive controller. The chapter concludes with an algorithm for advanced distributed model predictive control that is implementable on existing distributed building control architectures.