Sorin Bengea

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 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.

Implementation of model predictive control for an HVAC system in a mid-size commercial building

The article presents field experiment results from the implementation of a model predictive controller which optimizes the operation of a variable volume, dual-duct, multi-zone HVAC unit serving an existing mid-size commercial building. This full-scale proof-of-concept study was used to estimate the benefits of implementing advanced building control technologies during a retrofit. The control approach uses dynamic estimates and predictions of zone loads and temperatures, outdoor weather conditions, and HVAC system models to minimize energy consumption while meeting equipment and thermal comfort constraints. The article describes the on-line implementation of the hierarchical control system, including communication of the optimal control scheme with the building automation system, the controlled set-points and the component-level feedback loops, as well as the measured energy and indoor comfort performance benefits from the demonstration. The building-scale experiments and the receding-horizon control algorithm implementation results are described. Site measurements show this algorithm, when implemented in state-of-the-art direct digital control systems, consistently yields energy savings and reduces peak power while improving the indoor thermal comfort. The demonstration results show energy savings of 20% on average during the transition season, 70% on average during heating season, and 10% or more peak power reduction, all relative to pre-configured, rule-based schedules implemented in the retrofitted direct digital control system.

Parameter estimation of a building system model and impact of estimation error on closed-loop performance

Predictive-control methods have been recently employed for demand-response control of building and district-level HVAC systems. Such approaches rely on models and parameter estimates to meet comfort constraints and to achieve the theoretical system-efficiency gains. In this paper we present a methodology that establishes achievable targets for control-model parameter estimation errors based on closed-loop performance sensitivity. The control algorithm is designed as a Model Predictive Controller (MPC) that uses perturbed building-model parameters. We perform simulations to estimate the dependency of energy cost and constraint infringement time on the magnitude of these perturbations. The simulation results are used to define targets for the parameter estimation errors, which in turn are applied to specify the character of excitation and model structure used for identification. We design a parameter estimator and perform Monte-Carlo simulations for a model that includes sensor noise and load uncertainty. The distribution of the estimation errors are used to demonstrate that the established targets are met.

Simulation and experimental demonstration of model predictive control in a building HVAC system

This article presents the framework and results of implementing optimization-based control algorithm for building HVAC systems and demonstrates its benefits through reduced building energy consumption as well as improved thermal comfort along with lessons learned. In particular, a practically effective and computationally efficient model predictive control algorithm is proposed to optimize building energy usage while maintaining thermal comfort in a multi-zone medium-sized commercial building. This article has two themes. Driven by the challenge of fully evaluating the benefit of the proposed model predictive controller against baseline control, a model predictive control design framework is first presented with its performance benchmarked based on a high-fidelity building HVAC simulation environment to verify its effectiveness and feasibility. Following the same model predictive control design framework, the experimental results from the same building located at the Philadelphia Navy Yard are then presented. For the simulation study, the performance of the model predictive control algorithm was estimated relative to baseline days with exactly the same internal loads and outdoor conditions, and it was estimated that model predictive control reduced the total electrical energy consumption by around 17.5%. For the subsequent experimental demonstration, the performance of the model predictive control algorithm was estimated relative to baseline days with similar outdoor air temperature patterns during the cooling and shoulder seasons, and it was concluded that model predictive control reduced the total electrical energy consumption by more than 20% on average while improving thermal comfort in terms of zone air temperature.

Fault-tolerant optimal control of a building HVAC system

This article presents the development and application of a fault-tolerant control technology, its online implementation, and results from several tests conducted for a large-sized HVAC system. By integrating model-based model predictive control and data-driven fault detection and diagnosis algorithms, the technology automatically adapts the HVAC control laws to a set of subsystem faults and can therefore reach and maintain the largest energy consumption reduction levels that are achievable at any point throughout a building lifecycle. The model predictive control algorithm generates optimal set-points that minimize energy consumption for the HVAC actuator loops while meeting equipment operational constraints and occupant thermal comfort constraints. The fault detection and diagnosis algorithm uses probabilistic graphical models to detect and classify in real time potential faults of the HVAC actuators based on data from multiple sensors. The fault-tolerant control system is realized by executing the two algorithms on the same platform, within the same framework, and by using the fault detection and diagnosis algorithms output to continuously update the model predictive control algorithm constraints. The proposed integrated technology is executed at the supervisory level in a hierarchical control architecture as an extension of a baseline building management system. The performance and limitations of the fault detection and diagnosis, model predictive control, and fault-tolerant control algorithms are illustrated and discussed using measurement data recorded from multiple tests.