Student(s): Colin Jacobs, Norman Zvenika
Project: Using ML and Data Scraping for Equitable Resilient Energy Systems | View Poster (PDF)
Major(s): Computer Science and Business, Biocomputational Engineering
Advisor(s): George Witmer
Abstract
In 1996, Pennsylvania deregulated its energy market, allowing private companies to offer competitive electricity plans alongside default service rates. While this was intended to increase competition and consumer choice, the complex pricing structures introduced by competitive suppliers disadvantage households without the resources needed to compare electricity plans effectively. As a result, many consumers pay higher energy costs or enter into unfavorable service agreements.
This research develops methods for systematically comparing default and competitive electricity plans and implements them in an accessible tool to help consumers compare available electricity plans.
To achieve our objectives, we constructed a database of default and competitive supplier electricity plans from 2014 to 2024. The database was validated against publicly available resources. After validation, we analyzed the data to identify invariant features that make default plans comparable to competitive plans. These insights were integrated into an interactive, multilingual web dashboard that is publicly accessible.
We observed that public energy databases are often outdated. Our analysis identified five invariant features—fixed rate, 12-month term, no monthly charge, no early termination fees, and residential plans—enable effective plan comparisons. The interactive web dashboard displays comparable plans while allowing users to adjust these settings based on their preferences.
In conclusion, publicly available energy databases are often outdated, limiting their reliability for consumer decision-making. Our tool incorporates key comparison features to help consumers evaluate available energy plans. Future work will analyze comparable competitive supplier plans to generate insights that inform policies protecting consumers and ensuring fair market practices.
About Colin Jacobs
Colin Jacobs is currently pursuing a degree in Computer Science and Business at Lehigh University, where he has gained considerable experience as both a software engineer and undergraduate researcher. Colin specializes in developing high-performance applications, particularly leveraging parallel computing techniques to significantly enhance computational efficiency under the guidance of Professor Arielle Carr.
In addition to his technical expertise, Colin has engaged in research under Professor Alberto Lamadrid, focusing on the residential electricity market in Pennsylvania. His research involved utilizing advanced data scraping methods and machine learning algorithms to analyze electricity pricing structures. Specifically, Colin examined default energy plans provided by distributors in comparison to their alternative offerings, aiming to identify potential discrepancies. The objective of this research was to generate actionable insights that could inform government regulation, thereby protecting consumers from predatory pricing practices.
About Norman Zvenika
Norman is a senior at Lehigh University pursuing dual degrees in Computer Science and Biocomputational Engineering. He is passionate about using data science to address critical human needs. His research with Professor Lamadrid applies data engineering and data science techniques to systematically analyze Pennsylvania's electricity market, comparing default distributor energy plans with competitive supplier offerings. This work generates actionable insights to inform government regulations and protect consumers from potential predatory pricing practices. Beyond research, Norman mentors fellow engineering students at Lehigh and volunteers as a technician at the Salvation Army, Bethlehem Corps.