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Decentralized economic model predictive control for energy efficiency in a multi-zone building
Energy shortage is a challenge for many countries, and building energy consumption accounts for a considerable proportion of global energy consumption. The main work of this paper is to optimize the energy consumption of heating, ventilating, and air conditioning (HVAC) systems in buildings based on economic model predictive control (EMPC). The cost in EMPC design includes energy consumption and predicted mean vote (PMV), which is an index that evaluates the thermal comfort of indoor occupants. In order to model the nonlinearity of the PMV index, we propose a lattice piecewise linear (PWL) approximation, which has high approximation precision and facilitates the resulting optimization problem, which is basically a piecewise quadratic programming problem. For the piecewise quadratic programming, we propose a descent algorithm that converges quickly and scales well with the length of the prediction horizon in the EMPC problem. The experimental results demonstrate that the proposed method saves 19.78% of the electricity cost compared to the conventional control strategy and significantly increases indoor comfort.
Note to Practitioners
— The motivation of this article is to provide a control strategy to reduce building energy consumption and ensure indoor thermal comfort. In most of the existing methods for air conditioning temperature control, the occupants’ comfort hasn’t been considered. In this paper, thermal comfort is described by the PMV index, which is basically nonlinear. In order to model the thermal comfort more accurately, in this paper, the PMV index is approximated piecewise linearly in order to meet the requirements of accuracy and computational efficiency. The resulting optimization problem is not hard to solve, and we provide an efficient algorithm for solving this optimization problem. Preliminary simulation experiments demonstrate that this approach is practical, i.e., it achieves energy reduction and ensures thermal comfort. Our strategy, however, has not yet been deployed in real buildings. In future research, we will propose similar techniques for large-scale systems in order to solve energy optimization problems containing multiple thermal zones and realize the proposed technique in real buildings.
Matlab/Simulink is known in a large number of fields as a powerful and modern simulation tool. In the field of building and HVAC simulation its use is also increasing. However, it is still believed to be a tool for small applications due to its graphical structure and not to fit well for the simulation of multizone buildings. This paper presents the development of a new multizone building model for Matlab/Simulink environment, implemented into the SIMBAD Building and HVAC Toolbox. It's general enough to model a variety of useful cases. Conforming to the Simulink philosophy, the model is modular and structured into blocks to represent the modelled phenomena. To simplify the description of the simulated building, a graphical user interface SIMBDI is developed in parallel, generating an xml building description file. This file can be read directly by the SIMBAD multizone building model. Finally, a simulation case is presented in order to compare the new model with the Trnsys multizone building model.
Heating, Ventilation and Air Conditioning (HVAC) systems are installed in commercial buildings to meet the actual demand of heating and cooling at a given time such that the thermal comfort of occupants within the rooms (or zones) can be ensured. This is achieved by feeding air into the zones at a constant temperature using variable air volume (VAV) boxes, which offers an energy-efficient solution for a multi-zone building. The amount of supply air is controlled with the help of dampers located within the VAV boxes. Any occurrence of a fault in VAV dampers may cause undesirable results such as discomfort to occupants and a loss in energy efficiency. In this paper, we propose a nonlinear observer-based fault diagnosis method to extract a precise information about the fault so that subsequently, the controller can take corrective actions ensuring the thermal comfort of the occupants. The novelty of this method lies in its simple construction, which makes it applicable for diagnosing a wide variety of actuator faults in HVAC systems. The effectiveness of the proposed method is successfully demonstrated on a case-study of a one-storey building comprising of three zones, constructed using SIMBAD (SIMulator of Building And Devices).
Buildings are dynamical systems with several control challenges: large storage capacities, switching aggregates, technical and thermal constraints, and internal and external disturbances (occupancy, ambient temperature, solar radiation). Conflicting optimization goals naturally arise in buildings, where the maximization of user comfort versus the minimization of energy consumption poses the main trade-off to be balanced. Model predictive control (MPC) is the ideal control strategy to deal with such problems. Especially the knowledge and use of future disturbances in the optimization makes MPC such a powerful and valuable control tool in the area of building automation. MPC compromises a class of control algorithms that utilizes an online process model to optimize the future response of a plant. The main benefits of MPC are the explicit consideration of building dynamics, available predictions of future disturbances, constraints, and conflicting optimization goals to provide the optimal control input. MPC technology has been applied to process control for several decades and it is an upcoming field in building automation. This is a consequence of the large potential for saving energy in buildings and also allows to maximize the use of renewable energy sources. Furthermore, the added flexibility enables to integrate such buildings in future smart grids. In this work ten questions concerning model predictive control for energy efficient buildings are posed and answered in detail.
Variable air volume (VAV) boxes are used in multi-zone buildings, which varies the volume of constant temperature air separately supplied by an air-handling unit to meet challenging thermal load demands of individual zones. A fault appearing in the VAV damper severely affects the control performance and consequently, the energy efficiency of the overall system. To address this issue, we present a novel online redesign based fault-tolerant control strategy in this paper for a VAV thermal system in a multi-zone building. The novelty of the proposed strategy is that no a-priori information about the process is used in real-time. In fact, no online identification of the thermal dynamical model of the building process or estimation of its operating status (healthy or faulty) is done. The design of the fault accommodation scheme is solely based on the real-time trajectories generated by the process. In order to demonstrate the effectiveness of the developed strategy, we consider a case-study of three-zones in a one-story building.
This paper studies the problem of decentralized control design for thermal control in buildings, to achieve a satisfactory trade-off between underlying performance and robustness objectives. An output-feedback, model predictive framework is used for decentralized control which is based on a reduced order system representation. It entails the use of decentralized extended state observers to address the issue of unavailability of all states and disturbances. The decision on control architecture selection is based on an agglomerative clustering methodology developed previously [22]. The potential use of the proposed control design methodology is demonstrated in simulation on a multi-zone building, which quantifies the tradeoffs in performance and robustness with respect to the degree of decentralization.
The use of MATLAB toolbox YALMIP to model and solve optimization problems occuring in systems in control theory was discussed. The toolbox makes development of control oriented SDP problems. Rapid prototyping of an algorithm based on SDP can be done using standard MATLAB commands. YALMIP automatically detects the kind of a problem the user has defined, and selects a suitable solver based on this analysis.
The objective of this study is to demonstrate the effectiveness of model predictive control (MPC) in reducing the energy and demand costs for buildings in an electricity grid with time-of-use pricing and demand charges. A virtual model for a single floor, multi-zone commercial building equipped with a variable air volume (VAV) cooling system is built by Energyplus. Real-time data exchange between Energyplus and Matlab controller is realized by introducing the building controls virtual test bed (BCVTB) as a middleware. System identification technique is implemented to obtain the zone temperature and power model, which are to be used in the MPC framework. MPC with an economic objective function is formulated as a linear programming problem and solved. Pre-cooling effect during off-peak period and autonomous cooling discharging from the building thermal mass during on-peak period can be observed in a continuous weekly simulation. Cost savings brought by MPC are given by comparing with the baseline and other pre-programmed control strategies.
Observers can easily be constructed for those nonlinear systems which can be transformed into a linear system by change of state variables and output injection. Necessary and sufficient conditions for the existence of such a transformation are given.