Modelling and predictive control techniques for building heating systems

Simple item page
dc.check.embargoformatNot applicableen
dc.check.infoNo embargo requireden
dc.check.opt-outNoen
dc.check.reasonNo embargo requireden
dc.check.typeNo Embargo Required
dc.contributor.advisorLightbody, Gordonen
dc.contributor.authorO'Dwyer, Edward
dc.contributor.funderIrish Research Councilen
dc.contributor.funderUnited Technologies Research Center, Irelanden
dc.date.accessioned2017-01-16T15:32:16Z
dc.date.available2017-01-16T15:32:16Z
dc.date.issued2016
dc.date.submitted2016
dc.description.abstractModel predictive control (MPC) has often been referred to in literature as a potential method for more efficient control of building heating systems. Though a significant performance improvement can be achieved with an MPC strategy, the complexity introduced to the commissioning of the system is often prohibitive. Models are required which can capture the thermodynamic properties of the building with sufficient accuracy for meaningful predictions to be made. Furthermore, a large number of tuning weights may need to be determined to achieve a desired performance. For MPC to become a practicable alternative, these issues must be addressed. Acknowledging the impact of the external environment as well as the interaction of occupants on the thermal behaviour of the building, in this work, techniques have been developed for deriving building models from data in which large, unmeasured disturbances are present. A spatio-temporal filtering process was introduced to determine estimates of the disturbances from measured data, which were then incorporated with metaheuristic search techniques to derive high-order simulation models, capable of replicating the thermal dynamics of a building. While a high-order simulation model allowed for control strategies to be analysed and compared, low-order models were required for use within the MPC strategy itself. The disturbance estimation techniques were adapted for use with system-identification methods to derive such models. MPC formulations were then derived to enable a more straightforward commissioning process and implemented in a validated simulation platform. A prioritised-objective strategy was developed which allowed for the tuning parameters typically associated with an MPC cost function to be omitted from the formulation by separation of the conflicting requirements of comfort satisfaction and energy reduction within a lexicographic framework. The improved ability of the formulation to be set-up and reconfigured in faulted conditions was shown.en
dc.description.sponsorshipIrish Research Council (Enterprise Partnership Scheme)en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Version
dc.format.mimetypeapplication/pdfen
dc.identifier.citationO'Dwyer, E. 2016. Modelling and predictive control techniques for building heating systems. PhD Thesis, University College Cork.en
dc.identifier.endpage202en
dc.identifier.urihttps://hdl.handle.net/10468/3472
dc.languageEnglishen
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2016, Edward O'Dwyer.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjectModel predictive controlen
dc.subjectData-driven modellingen
dc.subjectBuilding heating systemsen
dc.thesis.opt-outfalse
dc.titleModelling and predictive control techniques for building heating systemsen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePHD (Engineering)en
ucc.workflow.supervisorg.lightbody@ucc.ie
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
ODwyerEJ_PhDAbstract2016.pdf
Size:
23.88 KB
Format:
Adobe Portable Document Format
Description:
Abstract
Thumbnail Image
Name:
ODwyerEJ_PhD2016.pdf
Size:
6.7 MB
Format:
Adobe Portable Document Format
Description:
Full Text E-thesis
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
5.62 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Doctoral Theses
College of Science, Engineering and Food Science - Doctoral Theses
Electrical and Electronic Engineering - Doctoral Theses