Use of model predictive control and weather forecasts for energy efficient building climate control

Theory and applications of HVAC control systems – A review of model predictive control (MPC)

Buildings all around the world consume a significant amount of energy, which is more or less one-third of the total primary energy resources. This has raised concerns over energy supplies, rapid energy resource depletion, rising building service demands, improved comfort life styles along with the increased time spent in buildings; consequently, this has shown a rising energy demand in the near future. However, contemporary buildings’ energy efficiency has been fast tracked solution to cope/limit the rising energy demand of this sector. Building energy efficiency has turned out to be a multi-faceted problem, when provided with the limitation for the satisfaction of the indoor comfort index. However, the comfort level for occupants and their behavior have a significant effect on the energy consumption pattern. It is generally perceived that energy unaware activities can also add one-third to the building’s energy performance. Researchers and investigators have been working with this issue for over a decade; yet it remains a challenge. This review paper presents a comprehensive and significant research conducted on state-of-the-art intelligent control systems for energy and comfort management in smart energy buildings (SEB’s). It also aims at providing a building research community for better understanding and up-to-date knowledge for energy and comfort related trends and future directions. The main table summarizes 121 works closely related to the mentioned issue. Key areas focused on include comfort parameters, control systems, intelligent computational methods, simulation tools, occupants’ behavior and preferences, building types, supply source considerations and countries research interest in this sector. Trends for future developments and existing research in this area have been broadly studied and depicted in a graphical layout. In addition, prospective future advancements and gaps have also been discussed comprehensively.

A review on optimized control systems for building energy and comfort management of smart sustainable buildings

Experimental analysis of model predictive control for an energy efficient building heating system

Model predictive control (MPC) has demonstrated exceptional success for the high-performance control of complex systems [1], [2]. The conceptual simplicity of MPC as well as its ability to effectively cope with the complex dynamics of systems with multiple inputs and outputs, input and state/output constraints, and conflicting control objectives have made it an attractive multivariable constrained control approach [1]. MPC (a.k.a. receding-horizon control) solves an open-loop constrained optimal control problem (OCP) repeatedly in a receding-horizon manner [3]. The OCP is solved over a finite sequence of control actions {u0,u1,f,uN- 1} at every sampling time instant that the current state of the system is measured. The first element of the sequence of optimal control actions is applied to the system, and the computations are then repeated at the next sampling time. Thus, MPC replaces a feedback control law p(m), which can have formidable offline computation, with the repeated solution of an open-loop OCP [2]. In fact, repeated solution of the OCP confers an "implicit" feedback action to MPC to cope with system uncertainties and disturbances. Alternatively, explicit MPC approaches circumvent the need to solve an OCP online by deriving relationships for the optimal control actions in terms of an "explicit" function of the state and reference vectors. However, explicit MPC is not typically intended to replace standard MPC but, rather, to extend its area of application [4]-[6].

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Stochastic Model Predictive Control: An Overview and Perspectives for Future Research

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.

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Model Predictive Control for the Operation of Building Cooling Systems

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.

Predictive Control for Energy Efficient Buildings with Thermal Storage

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

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.

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Model Predictive Control of thermal energy storage in building cooling systems

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

Yudong Ma

Papers

Model Predictive Control for the Operation of Building Cooling Systems

Predictive Control for Energy Efficient Buildings with Thermal Storage

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

Model Predictive Control of thermal energy storage in building cooling systems

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