Infor co-sponsors contest of student proposals for producing cleaner energy using data science, and presents Infor Technological Solutions Awards
Imagine the myriad ways big data could be instrumental in producing cleaner energy. How about a novel method for predicting photovoltaic potential, or simulating charging times for electric vehicles, or modeling optimal geographic locations for future wind farms?
Those were just a few of the winning concepts submitted by students in the High School Big Data Challenge this spring. Five Infor employees peer reviewed and helped select the winners, including those for the Infor Technological Solutions Awards, which Infor sponsored.
“The event challenged students to assess the technological, economical, and societal aspects of affordable and clean energy, using the power of data science,” said Sarah Schoonover, an Infor solutions architect who served as a judge.
The challenge was hosted by STEM Fellowship, a Canadian nonprofit dedicated to advancing STEM learning, mentorship, and experiential learning to equip the next generation of change-makers with skills in data science and scholarly writing. Experts, including five from Infor, selected 21 finalist teams to compete in the finale event: High School Big Data Day. The teams virtually showcased their research and defended their works on a professional level. The winning teams won cash prizes and an opportunity to publish their manuscripts in the STEM Fellowship Journal.
“It’s remarkable to see a record-breaking number of high school students from around the globe using their data analytics and machine learning skills to tackle the clean energy and sustainability problems from multiple angles with great results,” noted Justin Wang, an Infor senior product manager and judge.
“Having a background in renewable energy prior to joining Infor, I was drawn to the focus on using research data to provide insights or disprove assumptions in this space,” Schoonover said. “With most of the teams I met with — using data to break down a problem statement, look for trends, and take an evidence-based research approach to make a recommendation — muscles were exercised!”
Schoonover particularly enjoyed challenging the team from Hamilton, Canada, that used data to tackle the topic of “Investigating Existing Wind Turbine Fleet and Modeling Optimal Geographic Locations for Future Wind farms in Southern Ontario.” She said, “They used data analysis tools, neural network evaluations, trained models, k-means clustering, and heat maps to recommend seven specific geographic locations for untapped, potential wind farm development in their own province.”
“These events will motivate and attract new generations of scientists and engineers to leverage the advances on technologies to tackle complex global issues,” Infor senior data scientist Ehsan SadrFaridpour concluded.
Infor Technological Solutions Award winners
2nd Prize: $250
Investigating Existing Wind Turbine Fleet and Modeling Optimal Geographic Locations for Future Wind Farms in Southern Ontario
Andrew Kang, Liam Morrison, Henry Sun, and Andy Tang
Judges from Infor:
- Tugce Vural, Ph.D., decision scientist in applied innovation
- Luis V. Montiel, Ph.D., principal scientist
- Ehsan SadrFaridpour, Ph.D., senior data scientist
- Sarah Schoonover, solutions architect
- Justin Wang, senior product manager
- Coleman Artificial Intelligence
- Data Lake
- North America
Contact us and we'll have a Business Development Representative contact you within 24 business hours