Ir al contenido (pulsa Retorno)

Universitat Politècnica de Catalunya

    • Català
    • Castellano
    • English
    • LoginRegisterLog in (no UPC users)
  • mailContact Us
  • world English 
    • Català
    • Castellano
    • English
  • userLogin   
      LoginRegisterLog in (no UPC users)

UPCommons. Global access to UPC knowledge

60.175 UPC academic works
You are here:
View Item 
  •   DSpace Home
  • Treballs acadèmics
  • Màsters oficials
  • Dobles Màsters oficials
  • Doble Màster universitari en Enginyeria Industrial i Enginyeria de l'Energia (ETSEIB)
  • View Item
  •   DSpace Home
  • Treballs acadèmics
  • Màsters oficials
  • Dobles Màsters oficials
  • Doble Màster universitari en Enginyeria Industrial i Enginyeria de l'Energia (ETSEIB)
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Evaluation of model order reduction complexity for advanced control of commercial buildings

Thumbnail
View/Open
university-of-colorado-boulder-m-s-jose-angel-leiva-appendix.pdf (11,56Mb)
university-of-colorado-boulder-m-s-jose-angel-leiva-final-signed.pdf (9,440Mb)
Share:
 
  View Usage Statistics
Cita com:
hdl:2117/351319

Show full item record
Leiva Vilaplana, Jose Angel
Tutor / directorVan Wunnik, Lucas PhilippeMés informacióMés informacióMés informació; Marzullo, Thibault; Henze, Gregor P.
CovenanteeUniversity of Colorado Boulder
Document typeMaster thesis
Date2021-07-26
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
Commercial buildings are becoming smart elements that can interact with the power grid, participating in grid-level operations, meeting occupant needs and economic objectives, while enhancing the resiliency of power systems. Advanced controllers, such as model predictive control and reinforcement learning, are ideal for these novel applications which require optimal performance under varying boundary conditions, which traditional rule-based controllers are incapable of. In order to implement model predictive and reinforcement learning controllers, one of the main requirements is to have a suitable building model. However, the extensive engineering time and effort required for model development, together with the inaccuracies produced when modeling a complex real building, have delayed the deployment of advanced controllers. To overcome these difficulties, the development of accurate and computationally efficient models with low complexity, extracted from building operational data, emerges as a potential solution. The extraction of simplified models, or reduced order models, from operational data has been a key area of concern for researchers during the last decades, but its application has often been limited to specific systems, buildings, or test cases. There is a need for an open-source and standardized methodology that would render reduced order models readily available to controller designers. This would, in turn, foster the development of advanced control strategies and their application to real building systems. In this paper, a model order reduction workflow is presented as part of the development of an advanced control test bed that allows the development of advanced control algorithms for building applications. Here, several reduced order models have been created using a model order reduction method called numerical subspace state space system identification. Finally, these models have been used in the implementation of model predictive control and reinforcement learning control agents, demonstrating and showing the capabilities and functionalities of the proposed model order reduction workflow.
SubjectsIntelligent buildings -- Automatic control -- Mathematical models, Architecture and energy conservation, Edificis intel·ligents -- Control automàtic -- Models matemàtics, Arquitectura i estalvi d'energia
URIhttp://hdl.handle.net/2117/351319
Collections
  • Dobles Màsters oficials - Doble Màster universitari en Enginyeria Industrial i Enginyeria de l'Energia (ETSEIB) [12]
Share:
 
  View Usage Statistics

Show full item record

FilesDescriptionSizeFormatView
university-of-c ... e-angel-leiva-appendix.pdf11,56MbPDFView/Open
university-of-c ... gel-leiva-final-signed.pdf9,440MbPDFView/Open

Browse

This CollectionBy Issue DateAuthorsOther contributionsTitlesSubjectsThis repositoryCommunities & CollectionsBy Issue DateAuthorsOther contributionsTitlesSubjects

© UPC Obrir en finestra nova . Servei de Biblioteques, Publicacions i Arxius

info.biblioteques@upc.edu

  • About This Repository
  • Contact Us
  • Send Feedback
  • Inici de la pàgina