Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162466
Title: HVAC energy cost optimization for a multizone building via a decentralized approach
Authors: Yang, Yu
Hu, Guoqiang
Spanos, Costas, J.
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Yang, Y., Hu, G. & Spanos, C. J. (2020). HVAC energy cost optimization for a multizone building via a decentralized approach. IEEE Transactions On Automation Science and Engineering, 17(4), 1950-1960. https://dx.doi.org/10.1109/TASE.2020.2983486
Journal: IEEE Transactions on Automation Science and Engineering 
Abstract: It has been well acknowledged that buildings account for a large proportion of the world’s energy consumption. In particular, about 40%-50% of the building’s energy is consumed by the heating, ventilation and air-conditioning (HVAC). However, the energy use of buildings, especially the HVAC system, is far from being efficient. There still exists a dramatic potential to save energy through improve building energy efficiency. Therefore, this paper studies the control of HVAC system for multi-zone buildings with the objective to reduce the energy consumption cost of the HVAC system while satisfying the zone thermal comfort requirements. In particular, the thermal coupling due to the heat transfer between the adjacent zones are incorporated in the optimization. Considering that a centralized method is generally computationally prohibitive for buildings with an increasing number of zones, an efficient decentralized approach is developed, based on the Accelerated Distributed Augmented Lagrangian (ADAL) method [1]. To evaluate the performance of this decentralized approach, 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, to evaluate the performance and scalability, this decentralized approach is compared with the Distributed Token-Based Scheduling Strategy (DTBSS) [2], which has been demonstrated with a satisfactory performance in reducing the energy consumption of the HVAC system and in improving scalability. The numeric results reveal that when the number of zones in the buildings 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 average computation time of the two decentralized methods are compared in the case studies. The numeric results shows that the two decentralized methods are both computationally efficient. However, the decentralized approach proposed in this paper demonstrate better scalability with less average computation required.
URI: https://hdl.handle.net/10356/162466
ISSN: 1545-5955
DOI: 10.1109/TASE.2020.2983486
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TASE.2020.2983486.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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