LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
The emergence of Building Internet of Things (BIoT) technology as backbone for intra- and inter-building collaborations, and the recent advances in building technologies are expected to act as transformative enablers for energy smart connected communities. Architects are already moving toward connected buildings and commercial industry is advocating open space allocation practices using real-time data. Moreover, many cities have already started setting forth more stringent policies and regulations for clean air and protection of environment. For instance, some cities are already establishing guidelines and will soon be mandating Zero Net Energy (ZNE) building codes. Despite many challenges and barriers, these changes and advances are all good news for the power grid and the society as a whole; by the virtue of advanced data mining tools and control techniques the power grid will take advantage of lower quantity risks, and communities will be able to cut costs and engage in new business opportunities. The current building energy automation systems work in silos and are incapable of taking advantage of these advances and opportunities, community-based cooperation schemes and controls are not in existence. This work will fill some of the gaps in building and community controls and data mining tools and create a real-time information exchange loop between building communities.
The overarching goal of this dissertation is to develop novel advanced soft controls, collaboration schemes and forecasting and data mining tools that allow for buildings to connect and collectively plan and manage their energy loads. A simulation platform is developed to model different levels of energy systems such as buildings, building clusters, and DER. Building thermal behavior is captured via data-driven approaches and incorporated into optimization models to develop optimal setpoint controls that can also pre-heats or pre-cools for given zone(s) taking into account dynamic energy pricing, weather conditions, occupancy patterns, human comfort and business functions. This control strategy is extended to building community operation to achieve peak demand and energy consumption reduction at network level via load synchronization. Load synchronization and balancing between buildings in a community and between communities in a region will result in smoother aggregate load and load shifting to off-peak times, hence the average unit cost of electricity will go down. The proposed planning and control scheme will reduce energy and environmental footprints of communities and cities, create a better living and working environment for residents and occupants.
Subject (authority = RUETD)
Topic
Industrial and Systems Engineering
Subject (authority = local)
Topic
Energy
Subject (authority = LCSH)
Topic
Intelligent buildings -- Design and construction
Subject (authority = LCSH)
Topic
Smart power grids
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_10186
PhysicalDescription
Form (authority = gmd)
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xv, 175 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Subject (authority = LCSH)
Topic
Planned communities -- Energy consumption
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
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