Date of Award

8-2021

Document Type

Thesis

Degree Name

Master of Science (MS)

College/School

College of Science and Mathematics

Department/Program

Computer Science

Thesis Sponsor/Dissertation Chair/Project Chair

Michelle Zhu

Committee Member

Rui Li

Committee Member

Vaibhav Anu

Abstract

Demand response is a valuable tool for improving the reliability, stability, and financial efficiency of smart grids. With the intention of altering customer power consumption patterns, utility companies often implement strategies such as time-of-use (TOU) programs. Although effective in some situations, TOU programs struggle to perform in highly developed countries due to the complexity of human behavior. In this study, we analyze power consumption readings from smart meters from 5567 households in London, UK from November 2011 to February 2014 to measure the success of the TOU program. We additionally consider the variability of weather conditions and customer demographics when determining program outcome. We establish a relationship between time of day and low/high power consumption both in standard (STD) customers and TOU customers. Furthermore, we apply deep learning via a Long short-term memory (LSTM) model and determine predictability based on weather features through drill down operations.

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