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020 ▼a 9780438206540
035 ▼a (MiAaPQ)AAI10787057
035 ▼a (MiAaPQ)rpi:11267
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 621.3
1001 ▼a Minakais, Matthew.
24510 ▼a Identification and Iterative Learning Control for Building Systems: A Data-driven Approach.
260 ▼a [S.l.] : ▼b Rensselaer Polytechnic Institute., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 137 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Advisers: John Wen
5021 ▼a Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2018.
520 ▼a Commercial heating, ventilation, and air conditioning (HVAC) systems have been the focus of sustainability research due to their large energy footprint and relatively rudimentary control strategies. This thesis presents a novel approach to build
520 ▼a For simulation and system analysis, we model a multi-zone building as a lumped-parameter thermal resistance-capacitance network. This model is used to perform simulations and to study the inherent passivity in multi-zone building systems. We sho
520 ▼a For experimental valuation, we have designed, built, and instrumented a unique test facility. This intelligent building testbed was created to serve as a standardized platform for building modeling and control evaluation. Key features include wi
590 ▼a School code: 0185.
650 4 ▼a Electrical engineering.
690 ▼a 0544
71020 ▼a Rensselaer Polytechnic Institute. ▼b Electrical Engineering.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0185
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997378 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자