國立虎尾科技大學 |

Identification and Iterative Learning Control for Building Systems : = A Data-driven Approach.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Identification and Iterative Learning Control for Building Systems :/
Reminder of title:	A Data-driven Approach.
Author:	Minakais, Matthew.
Description:	1 online resource (137 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Contained By:	Dissertation Abstracts International79-12B(E).
Subject:	Electrical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780438206540

Identification and Iterative Learning Control for Building Systems : = A Data-driven Approach.
Minakais, Matthew.

Identification and Iterative Learning Control for Building Systems :A Data-driven Approach. - 1 online resource (137 pages)

Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.

Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2018.

Includes bibliographical references

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 building thermal control which utilizes weather forecasting and historic data to apply an iterative learning control (ILC) solution. ILC is shown to reject repeating disturbances and produce tight target trajectory tracking, so that setpoints may be strategically chosen to conserve energy and preemptively account for future disturbance. Furthermore, our approach does not require detailed model information, in contrast to, for example, the popular model predictive control (MPC) method.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438206540Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Identification and Iterative Learning Control for Building Systems : = A Data-driven Approach.
LDR:03518ntm a2200349Ki 4500 001 916665
005 20180927111921.5
006 m o u
007 cr mn||||a|a||
008 190606s2018 xx obm 000 0 eng d
020 $a 9780438206540
035 $a (MiAaPQ)AAI10787057
035 $a (MiAaPQ)rpi:11267
035 $a AAI10787057
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Minakais, Matthew. $3 1190469
245 1 0 $a Identification and Iterative Learning Control for Building Systems : $b A Data-driven Approach.
264 0 $c 2018
300 $a 1 online resource (137 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 $a Advisers: John Wen; Sandipan Mishra.
502 $a Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2018.
504 $a Includes bibliographical references
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 building thermal control which utilizes weather forecasting and historic data to apply an iterative learning control (ILC) solution. ILC is shown to reject repeating disturbances and produce tight target trajectory tracking, so that setpoints may be strategically chosen to conserve energy and preemptively account for future disturbance. Furthermore, our approach does not require detailed model information, in contrast to, for example, the popular model predictive control (MPC) method.
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 show the system to be strictly output incremental passive (SOIP) under closed loop negative feedback, which in turn implies convergence over iterations of ILC. For the ILC application, we match predicted disturbances to a historical database, and use well-matched days for learning. We also present a modified version of the algorithm to control both temperature and humidity using a single input, the mass flowrate. This algorithm is shown to converge to a family of solutions lying on the boundary of a set, the location and geometry of which can be designed to maintain comfort and reduce energy consumption.
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 wireless control and sensing of temperature, humidity, energy, and solar effects; and a modular structure with multiple zones and adjustable thermal and mass coupling. A key feature of this testbed is its placement inside a thermally-controlled enclosure, which can generate repeatable conditions. Simulation and experimental evaluations of the proposed modeling and control methods show improved setpoint tracking.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Rensselaer Polytechnic Institute. $b Electrical Engineering. $3 1184401
773 0 $t Dissertation Abstracts International $g 79-12B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10787057 $z click for full text (PQDT)