Energy Data Analytics Lab (2016-2017)
This Bass Connections project team furthered the mission of Duke’s Energy Data Analytics Lab to develop and apply tools to transform diverse energy data into innovative solutions for increasing the reliability, security, resilience and environmental sustainability of energy systems. Building on previous teams’ work, the 2016-17 team focused on automated building energy consumption from aerial imagery.
Detailed building-level energy consumption data for cities is rare due to the prohibitive collection costs, but could be used to identify and plan infrastructure and policy developments. Autonomous detection of objects such as buildings, roads, power lines and pipelines can be useful for policymakers to map infrastructure, track development patterns over time, find indicators of economic activity or quickly assess environmental damages.
Recent advances in computation for big data and image processing make it possible to learn about energy use in a fast and automated manner using machine learning. This project aimed to estimate building-level energy consumption from high-resolution aerial imagery by identifying buildings and extracting their properties (size, perimeter, etc.) and inputting these properties into an energy consumption estimation model. The model was designed using existing energy consumption data from the Department of Energy. To find an effective building detection technique, the team implemented both a traditional and a state-of-the-art deep learning classifier. Team members then applied their workflow to Gainesville, FL, to assess its effectiveness.
The team was able to demonstrate an approach to estimate building-level energy consumption given only high-resolution aerial orthoimagery. Overall energy estimation results for this study resulted in 7% error for a 2.25 km2 region. The team’s building detection approach identified over 80% of building pixels with fewer than 10% false detections.
Fall 2016 – Spring 2017
Artem Streltsov, Kyle Bradbury, Jordan Malof. “Automated Building Energy Consumption Estimation from Aerial Imagery.” 2018. International Geoscience and Remote Sensing Symposium 1676-1679.
Automated Building Energy Consumption Estimation from Aerial Imagery (Mitchell Kim, Sebastian Lin, Sophia Park, Eric Peshkin, Nikhil Vanderklaauw, Yue Xi, Samit Sura, Hoël Wiesner, Kyle Bradbury, Leslie Collins, Timothy Johnson; Bass Connections Poster Award, Audience Choice)
Aerial imagery object identification dataset for building and road detection, and building height estimation (dataset produced by Data+ team members)
Automated Building Energy Consumption Estimation from Aerial Imagery (final team presentation, April 21, 2017)
Automated Building Energy Consumption Estimation from Aerial Imagery (presentation by Eric Peshkin and Hoël Wiesner, Bass Connections Showcase, April 24, 2017)
Presentation to North Carolina legislators at the State Capitol, Graduate Education Day, May 16, 2017, Raleigh, NC (Hoël Wiesner)
This Team in the News
- Kyle Bradbury, Energy Initiative
- Leslie Collins, Pratt School of Engineering-Electrical & Computer Engineering
- Timothy Johnson, Nicholas School of the Environment-Earth and Ocean Sciences
/graduate Team Members
Samit Sura, Economics-AM
Hoel Wiesner, Master of Environmental Management, Energy and Environment, Geospatial Analysis
/undergraduate Team Members
Min Chul (Mitchell) Kim, Electrical & Computer Egr(BSE), Computer Science (BSE2)
Jer Sheng (Sebastian) Lin, Mathematics (BS)
Jee Hye (Sophia) Park, Electrical & Computer Egr(BSE)
Eric Peshkin, Mathematics (BS)
Nikhil Vanderklaauw, Mechanical Engineering (BSE)
Yue (Joyce) Xi, Computer Science (BS)
/yfaculty/staff Team Members
Richard Newell, Nicholas School of the Environment-Environmental Sciences and Policy
Guillermo Sapiro, Pratt School of Engineering-Electrical & Computer Engineering