Data+ summer projects help Duke undergraduates explore energy challenges

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Monday, Jan 06, 2020 - 9:39 am

Data+ is a 10-week summer research experience that welcomes Duke undergraduates interested in exploring new data-driven approaches to interdisciplinary challenges. Students join small project teams, collaborating with other teams in a communal environment. They learn how to marshal, analyze, and visualize data, while gaining broad exposure to the modern world of data science. Applications are due on Feb. 27, 2020. You can check out the Data+ Info Fair on Friday, January 17, when you'll have a chance to talk with project leads.

Three Data+ projects offered in 2020 will focus on energy:

Deep Learning for Rare Energy Infrastructures in Satellite Imagery

A team of students led by researchers in the Energy Data Analytics Lab, Electrical & Computer Engineering, and with participation from the Energy Access Project will investigate how to use synthetically-generated satellite imagery to improve the identification of energy infrastructure in satellite imagery. The detected energy infrastructure will fill outstanding data gaps in the ability to identify pathways for electrification in low-income countries. The team will build the foundation for research that can identify objects that appear relatively rarely in satellite imagery and accomplish this using very limited training examples by creating realistic synthetic 3D models of those rare objects. This would greatly scale up the applicability of computer vision techniques for energy object identification in overhead imagery.
Project Lead: Kyle Bradbury

Taking electrification on the road: Exploring the impact of the Electric Farm Equipment roadshow

A team of students led by researchers in the Energy Initiative and the Energy Access Project will explore historical data on the U.S. Electric Farm Equipment (EFE) demonstration show that ran between 1939 and 1941, which aimed to increase usage of electricity in rural areas. Students will compile data collected by the Rural Electrification Agency into a machine-readable form, and then use that data to explore and visualize the EFE’s impact. If time allows, they will then compare data from the EFE and a related, smaller-scale project from 1923 (“Red Wing Project”) to current data on appliance promotion programs in villages in East Africa that have recently gained access to electricity. The outcomes of this analysis would offer evidence on the successes and limitations of these types of programs, and the relevance of the historical U.S. case to countries that are currently facing similar challenges.
Project Leads: Victoria Plutshack, Jonathon Free, Robert Fetter

Forecasting campus energy usage for improved energy management

A team of students led by the Data and Analytics Practice at OIT will develop a robust forecasting model for predicting energy usage for different facilities on campus. Students will explore a wide range of real-world time-series data challenges from anomaly detection as well as handling, to benchmarking traditional statistical and modern machine learning models for forecasting. Students will also gain valuable experience developing an interactive application with latest open source libraries converting Jupyter notebooks into web applications to facilitate effective stakeholder collaboration. This work will enable several critical analyses for Duke Facilities Management to optimize their operations and significantly reduce costs.
Projects Leads: John Haws, Gagandeep Kaur