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Author ORCID Identifier


Open Access Dissertation

Document Type


Degree Name

Doctor of Philosophy (PhD)

Degree Program

Electrical and Computer Engineering

Year Degree Awarded


Month Degree Awarded


First Advisor

Jay Taneja

Second Advisor

David Irwin

Third Advisor

Prashant Shenoy

Fourth Advisor

Gabriel Cadamuro

Subject Categories

Data Science | Power and Energy


Over the past few years, the world has witnessed a dramatic increase in the efforts towards ending energy poverty. Out of the 17 Sustainable Development Goals (SDG) proposed by the United Nations (UN), the SDG 7 was specifically established to ensure affordable, reliable, sustainable and modern energy for all by 2030. As a result, governments and international organizations the world over have been investing millions of dollars into building new electric grid infrastructure and improving the quality and reliability of the existing ones. However, the methods and tools to track electrification efforts and monitor grid health -- reliability and power quality -- are inconsistent, inaccurate, expensive and often out of reach due to various budget, resource and political constraints. Without sufficient information about the grid, energy system planners struggle to identify the next set of areas to electrify, utilities struggle to provision reliable and good quality supply of electricity, and customers struggle to thrive. To address this shortcoming, in this thesis, we develop and demonstrate side channel measurement techniques for high resolution monitoring of electricity grids at scale. Specifically, our novel contributions include: (i) tracking electrification progress using high resolution daytime satellite images, (ii) monitoring power grid supply inconsistencies using daily nighttime lights satellite data, and (iii) measuring power quality at the end-points of distribution grid using digital cameras. We discuss the challenges associated with building deployable solutions for low-cost worldwide measurements of reliability and power quality, and particularly demonstrate our contributions towards estimating electricity reliability at a global scale.


Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Available for download on Sunday, September 01, 2024