東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Intelligent Control and Data-Driven ...

Shi, Jie.

Linked to FindBook

Google Book

Amazon

博客來

Intelligent Control and Data-Driven Algorithms for Critical Infrastructure Systems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Intelligent Control and Data-Driven Algorithms for Critical Infrastructure Systems./
Author:	Shi, Jie.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	288 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-11, Section: B.
Contained By:	Dissertations Abstracts International82-11B.
Subject:	Energy. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28318516
ISBN:	9798728226864

Intelligent Control and Data-Driven Algorithms for Critical Infrastructure Systems.
Shi, Jie.

Intelligent Control and Data-Driven Algorithms for Critical Infrastructure Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 288 p.

Source: Dissertations Abstracts International, Volume: 82-11, Section: B.

Thesis (Ph.D.)--University of California, Riverside, 2021.

This item must not be sold to any third party vendors.

The rapid development of computing devices and artificial intelligence (AI) in recent decades have dramatically reshaped the ecosystem of critical infrastructure systems. Intelligent control and data-driven algorithms have received widespread interests due to their great potential in reducing the operating costs and improving the system efficiency and reliability. The increasing data collected from different sectors of infrastructure provide abundant resources for scientific studies propelled by machine learning and statistics. Nevertheless, successful design and applications of novel intelligent algorithms on infrastructure can be challenging due to the complex domain-specific contexts and constraints therein. The goal of this dissertation is to investigate and design innovative solutions to emerging problems in power systems, transportation, energy efficient buildings, and wildfire camera networks. For power transmission systems, we developed automatic event detection and identification algorithms based on real-world synchrophasor data. For power distribution systems, we investigated and characterized the spatio-temporal correlation between distribution feeders with statistical models. We analyzed and quantified the impacts of different socioeconomic and weather factors on residents' electricity consumption. For transportation systems, we initially formulated the electric vehicle (EV) routing for ride-hailing services as a mixed integer programming problem. This framework does not scale well to large amount of EVs. To address this issue, we developed a reinforcement learning based algorithm to operate an EV fleet, which is characterized by decentralized learning and centralized decision making. For energy efficient buildings, we designed an innovative change-point logistic regression model to provide an accurate forecast of building occupancy. A novel building HVAC control algorithm, which aims at reducing the energy consumption, was developed by embedding the occupancy prediction model into a model predictive control framework. For wildfire camera networks, we developed an efficient video smoke detection framework designed for embedded applications on local cameras. We also proposed an optimal wildfire camera placement strategy by maximizing the overall camera network benefits with limited budget.

ISBN: 9798728226864Subjects--Topical Terms:

876794
Energy.
Subjects--Index Terms:

Power systems

Intelligent Control and Data-Driven Algorithms for Critical Infrastructure Systems.
LDR:03584nmm a2200397 4500 001 2285157
005 20211129123953.5
008 220723s2021 ||||||||||||||||| ||eng d
020 $a 9798728226864
035 $a (MiAaPQ)AAI28318516
035 $a AAI28318516
040 $a MiAaPQ $c MiAaPQ
100 1 $a Shi, Jie. $3 1935075
245 1 0 $a Intelligent Control and Data-Driven Algorithms for Critical Infrastructure Systems.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 288 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-11, Section: B.
500 $a Advisor: Yu, Nanpeng.
502 $a Thesis (Ph.D.)--University of California, Riverside, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a The rapid development of computing devices and artificial intelligence (AI) in recent decades have dramatically reshaped the ecosystem of critical infrastructure systems. Intelligent control and data-driven algorithms have received widespread interests due to their great potential in reducing the operating costs and improving the system efficiency and reliability. The increasing data collected from different sectors of infrastructure provide abundant resources for scientific studies propelled by machine learning and statistics. Nevertheless, successful design and applications of novel intelligent algorithms on infrastructure can be challenging due to the complex domain-specific contexts and constraints therein. The goal of this dissertation is to investigate and design innovative solutions to emerging problems in power systems, transportation, energy efficient buildings, and wildfire camera networks. For power transmission systems, we developed automatic event detection and identification algorithms based on real-world synchrophasor data. For power distribution systems, we investigated and characterized the spatio-temporal correlation between distribution feeders with statistical models. We analyzed and quantified the impacts of different socioeconomic and weather factors on residents' electricity consumption. For transportation systems, we initially formulated the electric vehicle (EV) routing for ride-hailing services as a mixed integer programming problem. This framework does not scale well to large amount of EVs. To address this issue, we developed a reinforcement learning based algorithm to operate an EV fleet, which is characterized by decentralized learning and centralized decision making. For energy efficient buildings, we designed an innovative change-point logistic regression model to provide an accurate forecast of building occupancy. A novel building HVAC control algorithm, which aims at reducing the energy consumption, was developed by embedding the occupancy prediction model into a model predictive control framework. For wildfire camera networks, we developed an efficient video smoke detection framework designed for embedded applications on local cameras. We also proposed an optimal wildfire camera placement strategy by maximizing the overall camera network benefits with limited budget.
590 $a School code: 0032.
650 4 $a Energy. $3 876794
650 4 $a Computer science. $3 523869
650 4 $a Artificial intelligence. $3 516317
650 4 $a Electrical engineering. $3 649834
653 $a Power systems
653 $a Energy efficient buildings
653 $a Camera networks
653 $a Transportation
653 $a Electrical vehicles
653 $a Energy consumption
690 $a 0984
690 $a 0544
690 $a 0800
690 $a 0791
710 2 $a University of California, Riverside. $b Electrical Engineering. $3 1669528
773 0 $t Dissertations Abstracts International $g 82-11B.
790 $a 0032
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28318516