A design approach for event-driven optimization in complex air conditioning systems
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Title of host publication | 2017 13th IEEE Conference on Automation Science and Engineering (CASE) |
Publisher | Institute of Electrical and Electronics Engineers, Inc. |
Pages | 912-917 |
ISBN (electronic) | 978-1-5090-6781-7 |
Publication status | Published - Aug 2017 |
Conference
Title | 13th IEEE Conference on Automation Science and Engineering, CASE 2017 |
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Place | China |
City | Xi'an |
Period | 20 - 23 August 2017 |
Link(s)
Abstract
Air conditioning (AC) systems take up the major proportion of total building energy consumption. While online optimal control is regarded as an efficient tool to improve the operating efficiency of AC systems, traditional online optimal control schemes utilize a so-called time-driven optimization (TDO) scheme. Although it works well for simple AC systems, several limitations are encountered when systems become more and more complex. TDO is basically a periodic scheme, which may lead to inefficient actions (e.g. delayed or unnecessary actions) in response to aperiodic or stochastic operational changes. TDO is also not efficient in balancing the optimization performance and computing load. Recently, an event-driven optimization (EDO) scheme has been proposed to solve these limitations. However, as the EDO in the building sector is quite a new topic, the corresponding EDO design methodology remains blank. Thus, this paper presents a feasible design methodology for EDO. The effectiveness of the design methodology is validated by the case study of a commercial AC system. Results show that the EDO (with optimized events) achieves better computational efficiency without sacrificing energy performance compared with the conventional TDO.
Citation Format(s)
A design approach for event-driven optimization in complex air conditioning systems. / Wang, Junqi; Lou, Siwei; Zhou, Pei et al.
2017 13th IEEE Conference on Automation Science and Engineering (CASE). Institute of Electrical and Electronics Engineers, Inc., 2017. p. 912-917.
2017 13th IEEE Conference on Automation Science and Engineering (CASE). Institute of Electrical and Electronics Engineers, Inc., 2017. p. 912-917.
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review