Data science approaches to data center sustainability and transportation predictive analytics

Date
2023
DOI
Authors
Tsiligkaridis, Athanasios
Version
Embargo Date
2025-01-16
OA Version
Citation
Abstract
Today's plethora of available data has brought about technological advancement in multiple facets of life. One area that has burgeoned is the development of intelligent cities that leverage technology to deliver improved quality of life. The concept of smart cities involves the incorporation of digital intelligence to urban systems with the hope that improvements in specific functions of a city can, all together, yield benefits in terms of resident satisfaction and the broader societal goals of efficiency and sustainability. In this dissertation, we study the advancement of smart cities through the development of the sub-areas of smart energy and transportation. Regarding smart energy, we focus on data center sustainability and data center inclusion in the smart grid to extract flexibility and ease congestion in various scenarios. We leverage queuing system theory along with optimization techniques to model data centers and propose demand response models in various settings where we highlight the benefits of data center inclusion in the smart grid. We first propose frameworks for data center interaction with an aggregator that attempts to extract flexibility from the data centers via a price incentive in a network-less setting. We further consider this setting for the case where data centers have the ability to service different types of jobs. Finally, we consider the network setting where data centers are included in the transmission grid with other loads and generators and highlight their ability to provide benefits to the grid. Regarding smart transportation, we focus on predictive analytics for transportation using machine learning and deep learning approaches. Specifically, we propose state-of-the-art transformer-based architectures for the task of destination prediction using artificial and real human movement data in both indoor and outdoor settings. Destination prediction involves the use of movement data to create intelligent models that can predict intended destinations given partial trajectory information. The use of such a system that knows in advance where a user intends to travel to can offer many functionalities. In an outdoor setting, tailored advertisements, adjacent points of interest, and open parking spots to a destination can be broadcasted to a user. In an indoor setting, a destination prediction system can allow for smart elevator control in large multi-floor buildings and the creation of density-based security systems that focus on high interest areas in a building. The contributions of this dissertation serve as strong building blocks for the development of smart cities and lead to multiple exciting directions for future work.
Description
License