It has been well acknowledged that buildings account for a large proportion of the world's energy consumption. However, the energy use of buildings, especially the heating, ventilation and air-conditioning (HVAC), is far from being efficient. There still exists a dramatic potential to save energy through improving building energy efficiency. Therefore, this paper studies the control of HVAC system for multi-zone buildings with the objective to reduce energy consumption cost while satisfying thermal comfort. In particular, the thermal couplings due to the heat transfer between the adjacent zones are incorporated in the optimization. Considering that a centralized method is generally computationally prohibitive for large buildings, an efficient decentralized approach is developed, based on the Accelerated Distributed Augmented Lagrangian (ADAL) method [1]. To evaluate the performance of the proposed method, we first compare it with a centralized method, in which the optimal solution of a small-scale problem can be obtained. We find that this decentralized approach can almost approach the optimal solution of the problem. Further, this decentralized approach is compared with the Distributed Token-Based Scheduling Strategy (DTBSS) [2]. The numeric results reveal that when the number of zones is relatively small (less than 20), the two decentralized methods can achieve a comparable performance regarding the cost of the HVAC system. However, with an increase of the number of zones in the building, the proposed decentralized approach demonstrates better performance with a considerable reduction of the total cost. Moreover, the decentralized approach proposed in this paper demonstrate better scalability with less average computation required.
Generally speaking, in most of the related works, the thermal couplings among the neighbouring or adjacent zones are ignored or not explicitly considered, which may lead to the degradation of performance in practice. On one hand, the actual energy cost of HVAC may rise due to the heat transfer between the neighbouring zones ignored in the optimization. On the other hand, the realistic thermal condition of each individual zone may deviate from the comfortable range. An attempt was reported in @cite_7 , which studied the distributed MPC strategy for HVAC system in a multi-zone building, where the thermal couplings between the neighbouring zones are explicitly discussed. To cope with the difficulties due to the nonlinearity and nonconvexity, a distributed ADMM method was applied based on some convexity approximation.
@cite_7
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This paper proposes a distributed MPC strategy for bilinear HVAC systems with coupled dynamics and constraints. These couplings arise from the heat transfer between adjacent zones and the limited total supply energy and airflow rate. To handle the coupled dynamics and constraints, a decoupled method is needed for the distributed computation of the online MPC problem. Besides the coupling issue, the bilinear model is also a fundamental obstacle to efficient online computation. To solve this problem, a convex relaxation method is developed in this paper. The performance of the proposed MPC strategy is demonstrated by a numerical example. Thermal hot spots and high temperature gradients degrade reliability and performance, and increase cooling costs and leakage power. In this paper, we explore the benefits of temperature-aware task scheduling for multiprocessor system-on-a-chip (MPSoC). We evaluate our techniques using workload characteristics collected from a real system by Sun's Continuous System Telemetry. We first solve the task scheduling problem statically using integer linear programming (ILP). The ILP solution is guaranteed to be optimal for the given assumptions for tasks. We formulate ILPs for minimizing energy, balancing energy, and reducing hot spots, and provide an extensive comparison of their thermal behavior against our technique. Our static solution can reduce the frequency of hot spots by 35 , spatial gradients by 85 , and thermal cycles by 61 in comparison to the ILP for minimizing energy. We then design dynamic scheduling policies at the OS-level with negligible performance overhead. Our adaptive dynamic policy reduces the frequency of high-magnitude thermal cycles and spatial gradients by around 50 and 90 , respectively, in comparison to state-of-the-art schedulers. Reactive thermal management strategies, such as thread migration, can be combined with our scheduling policy to further reduce hot spots, temperature variations, and the associated performance cost. The goal of maintaining users’ thermal comfort conditions in indoor environments may require complex regulation procedures and a proper energy management. This problem is being widely analyzed, since it has a direct effect on users’ productivity. This paper presents an economic model-based predictive control (MPC) whose main strength is the use of the day-ahead price (DAP) in order to predict the energy consumption associated with the heating, ventilation and air conditioning (HVAC). In this way, the control system is able to maintain a high thermal comfort level by optimizing the use of the HVAC system and to reduce, at the same time, the energy consumption associated with it, as much as possible. Later, the performance of the proposed control system is tested through simulations with a non-linear model of a bioclimatic building room. Several simulation scenarios are considered as a test-bed. From the obtained results, it is possible to conclude that the control system has a good behavior in several situations, i.e., it can reach the users’ thermal comfort for the analyzed situations, whereas the HVAC use is adjusted through the DAP; therefore, the energy savings associated with the HVAC is increased. Abstract Energy management of heating, ventilating and air-conditioning (HVAC) systems is a primary concern in building projects, since the energy consumption in electricity has the highest percentage in HVAC among all building services installations and electric appliances. Without sacrifice of thermal comfort, to reset the suitable operating parameters, such as the chilled water temperature and supply air temperature, would have energy saving with immediate effect. For the typical commercial building projects, it is not difficult to acquire the reference settings for efficient operation. However, for some special projects, due to the specific design and control of the HVAC system, conventional settings may not be necessarily energy-efficient in daily operation. In this paper, the simulation-optimization approach was proposed for the effective energy management of HVAC system. Due to the complicated interrelationship of the entire HVAC system, which commonly includes the water side and air side systems, it is necessary to suggest optimum settings for different operations in response to the dynamic cooling loads and changing weather conditions throughout a year. A metaheuristic simulation–EP (evolutionary programming) coupling approach was developed using evolutionary programming, which can effectively handle the discrete, non-linear and highly constrained optimization problems, such as those related to HVAC systems. The effectiveness of this simulation–EP coupling suite was demonstrated through the establishment of a monthly optimum reset scheme for both the chilled water and supply air temperatures of the HVAC installations of a local project. This reset scheme would have a saving potential of about 7 as compared to the existing operational settings, without any extra cost.