creator Bao, Zhen advisor Li, Zuyi The dissertation is composed of four parts: modeling demand response capability by internet data centers processing batch computing jobs, cloud storage based power consumption management in internet data center, identifying hot socket problem in smart meters, and online event detection for non-intrusive load monitoring without knowing label. Mathematical models are constructed to fulfill the research of the four targets, and numerical examples are used to test the effectiveness of the models. The first two parts optimize jobs in Data Center in order to find the best way of utilizing the existing computing resources and storage. Mixed-integer programming (MIP) is used in the formulation. The purpose of the third part is to identify the hot socket problem in smart meter. Machine learning method has been used to locate the bad installation of smart meters by analyzing historical data from smart meters. The fourth part is non-intrusive load monitoring for residential load in houses. Signal processing and deep learning methods are used to identify the specific loads from high frequency signals. Submitted by Erma Thomas (thomase@iit.edu) on 2017-11-02T23:35:26Z No. of bitstreams: 1 etdadmin_upload_497494.zip: 2149813 bytes, checksum: eea1fb6332ce90d89bf41f3a8ee2d56a (MD5) Made available in DSpace on 2017-11-02T23:35:26Z (GMT). No. of bitstreams: 1 etdadmin_upload_497494.zip: 2149813 bytes, checksum: eea1fb6332ce90d89bf41f3a8ee2d56a (MD5) Previous issue date: 2017-05 Ph.D. in Electrical Engineering, May 2017 2017 2017-05 http://hdl.handle.net/10560/4146 en Data Center Demand Response Machine Learning Non-Intrusive Load Monitoring Optimization Smart Meter Dissertation born digital application/pdf In Copyright http://rightsstatements.org/page/InC/1.0/ Restricted Access ECE / Electrical and Computer Engineering Illinois Institute of Technology Affiliated department