A Distributed Approach to Efficient Model Predictive Control of Building HVAC Systems
نویسندگان
چکیده
Model based predictive control (MPC) is increasingly being seen as an attractive approach in controlling building HVAC systems. One advantage of the MPC approach is the ability to integrate weather forecast, occupancy information and utility price variations in determining the optimal HVAC operation. However, application to largescale building HVAC systems is limited by the large number of controllable variables to be optimized at every time instance. This paper explores techniques to reduce the computational complexity arising in applying MPC to the control of large-scale buildings. We formulate the task of optimal control as a distributed optimization problem within the MPC framework. A distributed optimization approach alleviates computational costs by simultaneously solving reduced dimensional optimization problems at the subsystem level and integrating the resulting solutions to obtain a global control law. Additional computational efficiency can be achieved by utilizing the occupancy and utility price profiles to restrict the control laws to a piecewise constant function. Alternatively, under certain assumptions, the optimal control laws can be found analytically using a dynamic programming based approach without resorting to numerical optimization routines leading to massive computational savings. Initial results of simulations on case studies are presented to compare the proposed algorithms.
منابع مشابه
Investigation of Distributed Model Predictive Control for Economic Load Shifting in Building HVAC Systems
متن کامل
OPTIMIZATION-BASED MONITORING-SUPPORTED CALIBRATION OF A THERMAL PERFORMANCE SIMULATION MODEL
Building performance simulation is being increasingly deployed beyond the building design phase to support efficient building operation. Specifically, the predictive feature of the simulation-assisted building systems control strategy provides distinct advantages in view of building systems with high latency and inertia. Such advantages can be exploited only if model predictions can be relied u...
متن کاملDistributed Model Predictive Control for building HVAC systems: A Case Study
Model predictive control (MPC) in building HVAC systems incorporates predictions of weather and occupancy to determine the optimal operating setpoints. However, application of MPC strategies to large buildings might not be feasible in real time due to the large number of degrees of freedom in the underlying optimization problem. Decomposing the problem into several smaller sub-problems to be so...
متن کاملDistributed Model Predictive Control via Proximal
This paper investigates a distributed model predictive control (DMPC) framework for building control applications. The proposed framework is general in that it can be easily customized to solve the dynamic optimization problem for a broad class of multi-zone buildings with relatively complex HVAC systems. The Proximal Jacobian alternating direction method of multipliers (ADMM), a recent variant...
متن کاملPreventive Maintenance of Centralized HVAC Systems: Use of Acoustic Sensors, Feature Extraction, and Unsupervised Learning
In this paper, we propose a predictive maintenance scheme for centralized HVAC systems by autonomous monitoring and analyzing their acoustic emissions. Our proposed solution allows a building to be retrofitted to monitor its HVAC without having to modify the existing infrastructure. Our approach is to employ an energy-efficient, low-cost, and distributed acoustic sensing platform to capture and...
متن کامل