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ItemA FORMALIZED URBAN PROSUMER MODEL: SUPPORT OF AUTOMATED SIMULATION AND DESIGN OPTIMIZATION(Georgia Institute of Technology, 2021-07-26) Jung, Yun JoonMany global cities have announced ambitious net-zero energy consumption targets or net-zero CO2 emissions plans. It is well recognized that this can only be realized through a mix of measures such as efficiency improvements at the sites of consumption and decentralized energy generation, storage and delivery mechanisms. This transition will not happen without major changes to energy supply networks, especially in the way they enable frictionless inclusion of renewable energy sources and local supply, for instance through microgrids. At the urban scale, buildings constitute the major consumers of electricity and their integration through building-to-building and building-to-grid controls is crucial to realize efficient energy sharing in urban energy networks. Over the last decade, the building energy simulation domain has moved its focus from traditional local studies to urban energy studies. The main objective of this thesis is to make a contribution to this growing research domain, especially in enabling the simulation of energy supply networks in a robust manner and at a large scale. It is possible to simulate such networks with customized software but considering that there is no systematic way to specify urban energy models (especially with multiple concurrent control topologies), the simulation software has to be hand-customized which leads to opaque simulations that moreover are hard to use for rapid variant explorations. The thesis argues that this can be overcome by the development of an urban prosumer (UP) schema that facilitates the specification and automated mapping of an urban energy network into simulations, focusing on the effective specification of controls outside the software. At a high level, the UP schema is comprised of a physical and a logical layer. The physical layer conceptualizes existing urban energy networks using directed graphs for energy transport between nodes. The logical layer conceptualizes how the dynamic processing (reasoning) of sensor data leads to instructions to a set of actuators that execute the control. In doing so, two levels of control are distinguished: (a) “private” (mostly rule-based) control such as the internal HVAC system following temperature setpoints, (b) “public” control that is exposed to the rest of the network and thus within the scope of the UP schema. Public control can be either rule-based or optimal control, the latter driven by an appropriate optimality criterion, defined at a network scale. In design situations, the optimality criterion is not limited to control variables but can also include design parameters, such as building design parameters, solar installation sizes, community battery size, and the number of EV charging stations. Mixed-integer non-linear programming (MINLP) is used to solve optimal control problems. The genetic algorithm is employed to solve design optimization problems. The case studies using the UP schema for ten Georgia Tech campus buildings are presented. The purpose of the case studies is to prove that the UP schema can facilitate simulations involving different levels of controls. The simulations target optimal energy decisions for the selected campus buildings in the presence of PV and electricity battery. Additionally, three residential buildings in California are chosen to investigate how the design and control parameters act together to avoid the power outage situation with the embedded UP schema in the simulation platform.
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ItemThe space allocation problem(Georgia Institute of Technology, 2020-07-23) Saha, NirvikIn the domain of architecture and planning, the space allocation problem (SAP) is a general class of computable problems which is employed by numerous design processes to assist in the generation of spaces of a layout and simultaneously satisfy design objectives. The SAP has eluded automation due to combinatorial complexity and geometric intractability. This thesis describes a computational framework for solving the SAP across multiple scales and domains of the application using reinforcement learning algorithms that generate spatial solutions with optimal space-activity relations. In this research, a broad range of computable problems are addressed across three scales of design processes, namely, space planning, site planning, and interactive networks of city blocks. This is achieved by identifying the role of SAP in generating the spatial output of a design process and compartmentalizing the SAP into computable tasks. Each task is mapped to a spatial model that consists of a set of geometric operations driven by optimization algorithms or numerical relations. These techniques are referred to as the space allocation techniques or SAT and developed as autonomous modules. Each spatial model invokes a specific set of SAT modules, in sequence, and the models can be connected to solve the desired SAP. The spatial models are integrated into a framework after considering the exclusivity of the task accomplished by the models, common methods, data structures, and the flow of information between models. It is proposed that the spatial output of large design processes is approximated by creating workflows of connected elemental models. A workflow can be reused to solve project-specific design problems by updating the inputs such as site boundary or project requirements and bylaws. The workflows support design exploration and provide iterative user interaction such that for a given problem, it is possible to study entirely different solutions, explore the downstream propagation of a design decision, generate alternatives or determine an exact solution. The workflow permits re-usability to evolve the design solutions over numerous similar projects. These features of the proposal lead to an explicit design process that helps in preserving the information regarding design decisions. The proposal provides a semi-automation environment that allows users to develop spatial solutions, interactively, where the geometric or topological inputs can be altered at runtime and the system generates the solution. To evaluate this proposal, several features of a standard solution are identified to address their usability in design processes, the generalization of SAP across geometric and topologically variant problems, and the diverse scales of design processes. These features aid in the development of an alternative design environment where the potential for semi-automation is explored. The case studies and test-cases presented in this thesis illustrate the interaction between the designer and the software where the user can alter basic inputs on a spreadsheet or change the governing shapes at runtime and the workflow dynamically updates the internal organization of spaces and their activities. A prototype, namely, Integrated Design Framework or IDF, is implemented to demonstrate the applications of the proposed computational framework. It is anticipated that the development of SAP will support several activities related to architectural practice and research including design in practice, analytical research, and the development/deployment of building systems and subsequent processes such as the design of mechanical systems and structural design.
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ItemAn Information Exchange Framework For BIM, BAS, And IoT Data Using Semantic Web Technologies(Georgia Institute of Technology, 2020-04-25) Tang, ShuWith digital technologies like Building Information Modeling (BIM), Internet of Things (IoT), and Building Automation System (BAS), an increasing amount of data is being created. Data silos in Architecture Engineering and Construction (AEC) industry emerged. The isolation between BIM-based building contextual information, IoT devices’ time-series data, and BAS metadata still exist. This research aims to develop a framework to facilitate information exchange between BIM-based building contextual data, IoT devices’ time-series data, and BAS metadata using Semantic Web technology. This research: i) conducts a comprehensive literature review on BIM and IoT integration based on domains of application and integration methods to summarized an optimal current approach; ii) proposes a framework which enables information exchange among semantically described building contextual data, BAS metadata, and time-series data; iii) the proposed framework uses BOT and BRICK schema to describe building contextual data and BAS metadata; iv) creates an MVD for BIM assisted BAS design and information exchange using BACnet and IFC use case; v) validates the framework with the use case and data from Georgia Tech campus buildings.