Token based scheduling for energy management in building HVAC systems

Radhakrishnan, Nikitha; Yang, Shuying; Su, Rong; Poolla, Kameshwar

doi:10.1016/j.apenergy.2016.04.023

Cited by 57 publications

(50 citation statements)

References 35 publications

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“…We find that this decentralized approach can almost approach the optimal solution of the problem. Moreover, we compare the decentralized approach with the DTBSS method [2]. The numeric results reveal that the decentralized approach proposed in this paper demonstrates better performance both in reducing the cost of the HVAC systems as well as in improving the computational efficiency when the number of zones is large.…”

Section: Hvac Energy Cost Optimization For a Multi-zonementioning

confidence: 97%

“…In [26], to protect privacy, a decentralized method based on a multi-level virtual market was proposed. Different from the above works where the heat transfer between neighbouring thermal zones is ignored, [2], [27] regarded the heat transfer from the adjacent zone as external thermal disturbances that are measured through sensors or learned from data at the beginning of each planning horizon. To cope with the difficulties and challenges to solve the problem, a hierarchical distributed method was proposed, in which the original optimization problem was divided into three-level subproblems and each corresponds to a part of the global objective function.…”

Section: Related Workmentioning

confidence: 99%

“…In particular, about 40%-50% of this consumption is attributed to heating, ventilation and air-conditioning (HVAC) systems [3]. In tropical countries, like Singapore, Malaysia, and India etc., the proportion of energy consumption caused by HVAC is even higher [2]. However, the energy consumption in buildings, especially the HVAC systems, is far from being efficient.…”

Section: Introductionmentioning

confidence: 99%

“…However, the energy consumption in buildings, especially the HVAC systems, is far from being efficient. There still exists dramatic potential to reduce unnecessary energy consumption through improving building energy efficiency [2].…”

Section: Introductionmentioning

confidence: 99%

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HVAC Energy Cost Optimization for a Multizone Building via a Decentralized Approach

Yong

Spanos

2020

IEEE Trans. Automat. Sci. Eng.

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It has been well acknowledged that buildings account for a large proportion of the worlds 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.Note to Practitioners-Buildings accounts for a large proportion of the worlds energy consumption, especially the HVAC system. How to improve the energy efficiency of HVAC system has been recognized as an important and urgent problem for a sustainable future. Motivated by this important problem, this paper is focused on the intelligent control of HVAC system for multi-zone buildings with the objective to reduce energy consumption cost for the HVAC system while maintaining zone thermal comfort. Considering that a centralized method usually encounters computation difficulties with a large number of zones, this paper aims to develop an efficient decentralized solution. In terms of the problem, there usually exist various couplings between different zones, which both arise from the heat transfer between the neighbouring zones and the operation limits of ).the HVAC system. Moreover, this problem is nonconvex and nonlinear. Therefore, it is generally difficult to find an existing decentralized or distributed method, which are mostly established for convex optimization problems. Motivated by the recent progress on decentralized or distributed optimization for nonconvex problems, this paper proposes an efficient decentralized approach, which mainly contains three steps. In the first step, the o...

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Section: Hvac Energy Cost Optimization For a Multi-zonementioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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HVAC Energy Cost Optimization for a Multizone Building via a Decentralized Approach

Yong

Spanos

2020

IEEE Trans. Automat. Sci. Eng.

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“…investigated the various constraints of HVAC scheduling and proposed a novel, token-based distributed control/scheduling approach that can account for varying indoor environment and occupant conditions [17]. Zhao, et al proposed a pattern recognition-based method to detect and diagnose faults in chiller operations, using a one-class classification algorithm [18].…”

Section: A Variety Of Fault Detection and Diagnosis (Fdd) Tools Have mentioning

confidence: 99%

Modeling of HVAC operational faults in building performance simulation

2017

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Operational faults are common in the heating, ventilating, and air conditioning (HVAC) systems of existing buildings, leading to a decrease in energy efficiency and occupant comfort. Various fault detection and diagnostic methods have been developed to identify and analyze HVAC operational faults at the component or subsystem level. However, current methods lack a holistic approach to predicting the overall impacts of faults at the building level-an approach that adequately addresses the coupling between various operational components, the synchronized effect between simultaneous faults, and the dynamic nature of fault severity. This study introduces the novel development of a fault-modeling feature in EnergyPlus which fills in the knowledge gap left by previous studies. This paper presents the design and implementation of the new feature in EnergyPlus and discusses in detail the fault-modeling challenges faced. The new fault-modeling feature enables EnergyPlus to quantify the impacts of faults on building energy use and occupant comfort, thus supporting the decision making of timely fault corrections. Including actual building operational faults in energy models also improves the accuracy of the baseline model, which is critical in the measurement and verification of retrofit or commissioning projects. As an example, EnergyPlus version 8.6 was used to investigate the impacts of a number of typical operational faults in an office building across several U.S. climate zones. The results demonstrate that the faults have significant impacts on building energy performance as well as on occupant thermal comfort. Finally, the paper introduces future development plans for EnergyPlus fault-modeling capability.

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Energy use analysis of the variable refrigerant flow (VRF) system versus the multi - split unit using TRNSYS

Yau

Amir

2019

Heat Mass Transfer

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Token based scheduling for energy management in building HVAC systems

Cited by 57 publications

References 35 publications

HVAC Energy Cost Optimization for a Multizone Building via a Decentralized Approach

HVAC Energy Cost Optimization for a Multizone Building via a Decentralized Approach

Modeling of HVAC operational faults in building performance simulation

Energy use analysis of the variable refrigerant flow (VRF) system versus the multi - split unit using TRNSYS

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