Staff View
Energy use forecast and model predictive control of building complexes

Descriptive

TitleInfo
Title
Energy use forecast and model predictive control of building complexes
Name (type = personal)
NamePart (type = family)
TaghizadehVaghefi
NamePart (type = given)
Seyedabolfazl
DisplayForm
Seyedabolfazl TaghizadehVaghefi
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Jafari
NamePart (type = given)
Mohsen A.
DisplayForm
Mohsen A. Jafari
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Brouwer
NamePart (type = given)
Jacob
DisplayForm
Jacob Brouwer
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
co-chair
Name (type = personal)
NamePart (type = family)
Baykal-Gursoy
NamePart (type = given)
Melike
DisplayForm
Melike Baykal-Gursoy
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Jeong
NamePart (type = given)
Myong-K.
DisplayForm
Myong-K. Jeong
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Lu
NamePart (type = given)
Yan
DisplayForm
Yan Lu
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2014
DateOther (qualifier = exact); (type = degree)
2014-10
Place
PlaceTerm (type = code)
xx
CopyrightDate (encoding = w3cdtf)
2014
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
U.S. households and commercial buildings consume approximately 40 percent of total energy conversion in the U.S. and account for 72 percent of total U.S. electricity consumption. Commercial building energy demand, in particular, doubled between 1980 and 2000 and has increased 50 percent since then. Developing innovative technologies and building energy-efficiency methods are therefore essential for U.S. national interests and a sustainable energy future. In this thesis, an optimal framework for forecasting and optimization of energy consumption for building complexes is developed. For forecasting purposes, a hybrid time series-regression model is introduced to combine regression models and seasonal autoregressive moving average models to accurately forecast energy usage at both the building and the community/campus level. For optimization purposes, this thesis proposes an optimal control strategy at the building level, which consists of two main phases. In the first phase, a set of offline data – either generated by a whole building simulation platform or measured from a real building – is used to develop models that capture the dynamic behavior of building energy usage. In the second phase, the models are fed into an optimization model that computes the optimal control variables of the building. The optimization model is a Multi-objective Dynamic Programing model that minimizes total operating energy cost and demand charges as well as total deviation from thermal comfort bounds. In addition, the proposed control strategy is adaptive, so that it updates both the estimation and the optimization steps as soon as it receives new measured data. A data-driven risk-based framework is also proposed to predict and control industrial loads in non-residential buildings. In this framework, a set of predictive analysis tools is employed to allocate industrial load profiles into a particular set of classes. Load profiles within the same class have lower variance and follow the same pattern. Then, a generalized linear model (GLM) is used to predict the probability of having stochastic industrial loads coming online over rolling time windows. Finally, for controlling demand response to avoid demand charges, the proposed framework provides the necessary tools to institute load shedding or load shifting strategies.
Subject (authority = RUETD)
Topic
Industrial and Systems Engineering
Subject (authority = ETD-LCSH)
Topic
Energy consumption--Forecasting
Subject (authority = ETD-LCSH)
Topic
Structural engineering
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_6004
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (viii, 118 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Seyedabolfazl TaghizadehVaghefi
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T3HT2QZ1
Genre (authority = ExL-Esploro)
ETD doctoral
Back to the top

Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
TaghizadehVaghefi
GivenName
Seyedabolfazl
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2014-10-03 16:00:11
AssociatedEntity
Name
Seyedabolfazl TaghizadehVaghefi
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
RightsEvent
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2014-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2015-05-02
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after May 2nd, 2015.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
Back to the top

Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
Back to the top
Version 8.4.8
Rutgers University Libraries - Copyright ©2022