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Advisory Committee Chair

Leon Jololian

Document Type

Dissertation

Date of Award

2023

Degree Name by School

Doctor of Philosophy (PhD) School of Engineering

Abstract

More than 9 billion people are expected to live on the planet in 2050, about two-thirds in cities. This substantial population growth in urban areas increases demands on cities' resources, services, and infrastructure. Hence, many cities leverage intelligent technologies to ensure urban communities' efficiency in integrating massive infrastructure components and services. Cities define this reconstruction of their operational framework as a smart city. Effective resource consumption management in a smart city integrative structure presents a problem with solutions concerning the distribution of city services. This dissertation aims to establish a conceptual framework to effectively manage resource consumption in a smart city using digital twin architecture for implementation. The smart city space's energy component serves as this research's focus. Modeling a smart city digital twin establishes the framework to mitigate risks and manage real-world problems. This research uses the case study method to test the smart energy subset of the smart city model development in conjunction with 1) actual data from residential customers in Austin, TX, 2) the Microsoft Azure platform to replicate IoT, digital twin, and machine learning aspects, and 3) review of pertinent literature related to the research question. Insights gained from this applied research effort can broaden understanding for application to smart city domains beyond energy, such as health, sustainability, and education. Keywords: Digital twin, Smart cities, Internet of Things (IoT)

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Engineering Commons

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