Solar Power Estimation

Project Summary

Sharrin ManorArjun DevarajanWuming Zhang, and Jeffrey Perkins explored a lage collection of imagery data provided by the U.S. Geological Survey, with the goal of identifying solar panels using image recognition. They worked closely with the Energy Data Analytics Lab, part of the Energy Initiative at Duke.

Themes and Categories
Year
2015

Project Results

The students coded their own proof-of-principle algorithm which identified solar panels in a small test set with over ninety percent accuracy. They also painstakingly created a ground-truthed dataset that will help train future machine-learning algorithms.

Download the executive summary (PDF).

Video: The students and their mentor talk about the project.

Disciplines Involved

  • Environmental Science
  • Energy Systems
  • Machine Learning
  • Electrical Engineering

Project Team

Undergraduates: Sharrin Manor, Wuming Zhang, Jeffrey Perkins, Arjun Devarajan

Faculty Sponsors:

 

Project Mentor: Kyle Bradbury, Managing Director, Energy Data Analytics Lab

Graduate Mentors:

Related People

Related Projects

The Air Force’s F-15E Strike Eagle jets have parts that wear down and break, causing unscheduled maintenance events that take away valuable time in the air for critical missions and training. Our team, Limitless Data, is working with Seymour Johnson Air Force Base to mine manually entered maintenance data to visualize and predict aircraft failures. We created a prototype data visualization product that will enable maintainers on the flight line and help them identify and repair critical failures before they happen, keeping jets ready to fly, fight and win.

 

Faculty Lead: Dr. Emma Rasiel

Client Lead: Lt. Devon Burger

Project Manger:  Vignesh Kumaresan

This project aims to improve the computational efficiency of signal operations, e.g., sampling and multiplying signals. We design machine learning-based signal processing modules that use an adaptive sampling strategy and interpolation to generate a good approximation of the exact output. While ensuring a low error level, improvements in computational efficiency can be expected for digital signal processing systems using the implemented self-adjusting modules.

Project Leads: Yi Feng, Vahid Tarokh

 

Click here to view the project team's poster

 

Watch the team's final presentation (on Zoom) here:

 

Mapping History has focused on the categorizing, labelling, digitization, and 3D reconstruction of 16th & 17th century maps & atlases of London and Lisbon. Over the course of the summer, the Mapping History team has developed its own unique analytical dataset by painstakingly labelling every element contained within these maps, used python to digitize this dataset, and, now in the projects final stage, has begun the process of reconstructing these historical perspectives in a 3D game engine.

Project Lead: Philip Stern, Ed Triplett

Project Manager: Sam Horewood

 

View the team's final poster here

Watch the team's final presentation (on Zoom) below: