Hello! My name is Zeal and I am a third year Ph.D. student in the Electrical & Computer Engineering department at University of Massachusetts Amherst. I work at the Systems Towards Infrastructure Measurement & Analytics (STIMA) lab, advised by Prof. Jay Taneja. I hold a master’s degree in Energy Science, Technology & Policy with concentration in ECE from Carnegie Mellon University. My research at UMass Amherst is focused on harnessing the power of data and technology for developing low-cost energy infrastructure monitoring and management solutions. Additionally, my work also involves using mobile systems, remote sensing, machine learning & deep learning for acquiring data and information in low-data settings.
Graduate Research Assistant
- Research Focus: Data science and AI for social good, technology for development, remote sensing, energy analytics.
AI Engineering Intern
- Applied machine learning to satellite data to develop a new data layer - monthly electrification maps with spatial resolution of 0.5km for the continent of Africa, from 2012 to present.
- Built prototype of energy consumption data layer that used electrification maps, publicly available satellite data and surveys to estimate tiers of energy consumption at a spatial resolution of 0.5km.
- Developed the aforementioned data layers to improve the market insights being delivered to company's clients and to supplement data used for training company's proprietary ML models.
- Gained experience in building end-to-end data pipelines using Google Cloud products - BigQuery, Bucket, Compute Engine, and a geospatial data analysis platform - Google Earth Engine.
Data Science Intern
- Developed a monitoring tool using Grafana and SQL for real-time monitoring of deployed smart meters, base stations and cloud services to facilitate efficient troubleshooting.
- Analyzed smart meter data to track the evolution of electricity quality and reliability across 68 sites spread over Sub-Saharan Africa and South-Asia with 10 to 500+ customers per site.
- Provided need based data and analysis support to different teams.
Engineering Intern (Remote)
- Developed an optimal battery dispatching algorithm to minimize the operating cost of residential solar grid+storage system by controlling charging & discharging of the battery.
- Assisted in development of short-term load forecasting algorithm for company’s residential energy management system.
Graduate Teaching Assistant
- Head TA for two senior level courses: Fundamentals of Power Systems and Embedded Systems.
- Delivered technical lectures on modeling & simulation of power systems, and solving computational power systems problems like optimal power flow in MATLAB.
Data Science Intern
- Developed a suite of interactive analytical reports that provide actionable commercial, financial and technical insights into grid operations to company’s utility customers.
- Created an outlier detection and removal program to filter noise recorded by smart meters.
Deep Learning for Measuring Density of Different Classes of Buildings in Satellite Images
Estimate density of residential, commercial and industrial buildings in daytime satellite images using deep learning, to support energy demand prediction efforts in emerging economies.
Monitoring Electric Grid Reliability Using Satellite Data
Develop, evaluate and improve novel electric grid reliability measurement indices using nighttime-lights satellite data to enable grid reliability monitoring at a global scale; create an API to make indices publicly accessible.
GridInSight 2.0: Monitoring Electricity Using Visible Lights
Develop a non-intrusive grid sensing system using machine vision cameras to monitor power quality of the electric grids at the edges of the distribution network in real time; evaluate and improve the system to make it deployable at scale.
Mapping Disasters & Tracking Recovery in Conflict Zones Using Nighttime Lights
- Developed a technique to generate high resolution (~0.5km) disaster maps of a region as soon as one day after the crisis begins, to deliver up-to-date information regarding spatial extent of the disaster to disaster managers.
- Demonstrated a method to precisely identify the day when bombing occured by tracking sudden changes in nigttime lights illumination.
- Detected and characterized electricity outages across a conflict-affected region; presented a case study on Sana'a - capital city of Yemen.
- Proposed and evaluated two recovery measurement indices to trace recovery of the region spatially and temporally using nigttime lights data.
- Published full paper in the proceedings of IEEE GHTC'20, October 2020. Link
GridInSight: Monitoring Electricity Using Visible Lights
Created a low-cost solution to non-intrusively monitor grid power quality and phase using smartphone cameras to facilitate better management of grids in developing countries.
- Demonstrated the use of cell phone and machine vision cameras to non-intrusively monitor electric-grid frequency and phase with errors of 1-2%, and 2-10%, respectively.
- Developed a novel technique that uses cameras to passively monitor voltage, and obtained an error of 8-15% for measuring voltage of a light bulb that our system had not seen previously.
- Published full paper in the proceedings of ACM BuildSys'19, November 2019. Link
Smart Metering Data For Tracking Access to Electricity
- Analyzed smart meter data to track the evolution of electricity availability, reliability, quality, and grid capacity across 68sites spread over Sub-Saharan Africa and South-Asia with 10 to 500+ customers (smart meters) per site.
- Quantified the growth of energy access across sites by linking smart meter data analysis to World Bank’s Multi-tier Framework for household electricity supply.
- Presented by SparkMeter at the 7th Microgrid Innovation Forum, September 2018.
Selected Course Projects
Multi-tier Online Book Store
Developed a multi-tier web application using Flask in Python and added features like caching, replication, load-balancing, fault tolerance and recovery. Link
Where, When and Watt?
Created a program to predict occupancy of different rooms based on appliance power consumption data and achieved 93% model prediction accuracy. Link
New York State Energy Brief
Analyzed multiple open-source datasets to study and predict NY’s energy consumption in residential, commercial, industrial and transportation sectors.
Solving Unit Commitment
Implemented mixed-integer linear programming (Branch & Bound method) to solve a 24-hour unit commitment problem using data from multiple generators and demand data.
Energy Policy & Economics
Studied and delivered policy evaluation write-ups on energy security, clean power plan, emissions from internationaltransportation, 100% renewable vs. 0 emissions, and renewable integration in regulated vs. deregulated electricity markets.
Analysis of Energy Transport & Storage in Switzerland
Studied and presented a detailed analysis of energy transmission and storage in Zürich and Canton of Valais; Proposed solutions to reduce congestion and achieve their future energy goals
- Zeal Shah, Feng-Chi Hsu, Christopher D. Elvidge, and Jay Taneja. “Mapping Disasters & Tracking Recovery in Conflict Zones Using Nighttime Lights.” In the IEEE Global Humanitarian Technology Conference (GHTC'20), October 2020. Link
- Zeal Shah, Alex Yen, Ajey Pandey, and Jay Taneja. “GridInSight: Monitoring Electricity Using Visible Lights.” In the 6th ACM International Conference on Systems for Energy-Efficient Built Environments, Cities, and Transportation (BuildSys’19), November 2019. Best Paper Nominee. Link
Posters & Presentations
- Zeal Shah, Jay Taneja. “Monitoring Electric Grid Reliability Using Satellite Data.” In the 6th ACM International Conference on Systems for Energy-Efficient Built Environments, Cities, and Transportation, November 2019. Best Poster Award. Link
- Zeal Shah, Alex Yen, Ajey Pandey, Jay Taneja. “GridInSight: Monitoring Electricity Using Visible Lights.” In the 2nd Annual ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS’19), July 2019. Link
- “Smart Metering Data For Tracking Access to Electricity.” SparkMeter's keynote at the 7th Microgrid Global Innovation Forum, September 2018. (Presented by Jon Thacker)
- Zeal Shah, Yoolhee Kim, Anand Prakash, Vasu Nambeesan. “Occupancy Prediction Based on the Power Consumption Patterns.” In the Carnegie Mellon University Symposium on Machine Learning in Science and Engineering, May 2017. Link
- Zeal Shah, Siddhartha Joshi. “Operation and Analysis of a Bi-directional DC-DC Converter for Efficient Charge Control of Battery in a Microgrid.” In the 50th IEEE Industry Applications Society Annual Meeting, October 2015.