Energy Data Analytics Lab (2015-2016)

There are increasingly many sources of energy data in the modern world. Examples include electric utility smart meters capable of providing minute-by-minute data from millions of buildings, power market data tracking real-time energy prices across the nation, satellite imagery of energy systems as well as thermostat and control system sensor data in buildings, among others. Based at the Duke University Energy Initiative, the Energy Data Analytics Lab’s mission is to develop and apply data analytics tools to transform diverse energy data into innovative solutions for increasing the reliability, security, resilience and environmental sustainability of energy systems, while reducing costs.

Through team-based problem-solving, members of this Bass Connections project team learned to describe the potential effects of data analytics tools on energy systems, mastered the fundamentals of machine learning and big data algorithms and began to implement and understand the implications of results from experimental research techniques.

Increased attention to the global warming crisis has led to the rapid adoption of distributed rooftop solar photovoltaics across the U.S. Simultaneously, object detection in imagery is now a common research tool with varying applications ranging from military surveillance to facial recognition on social media. With the recent proliferation of solar panels, remote object detection has become an increasingly attractive tool to help track solar installations nationwide.

Using a ground-truth dataset of over 19,000 solar arrays in Fresno, Modesto, Oxnard and Stockton, California, amassed by the 2014-2015 version of this project team, the 2015-2016 team developed a solar photovoltaics identification algorithm with 90.4% accuracy when extracting regions using the maximally stable extremal regions (MSER) technique, and with 99.5% accuracy when using regions formed from the area surrounding solar arrays in the ground-truth training set.

Because both methods share preprocessing, feature extraction and classification techniques, but differ in region extraction, the team identified region extraction to be the key cause of the discrepancy, and thus, the main constraint in the overall PV identification process.

In Summer 2016, a Data+ team took this work further by building a ground truth dataset comprising satellite images, building footprints, and building heights (LIDAR) of more than 40,000 buildings, along with road annotations. This dataset can be used to train computer vision algorithms to determine a building’s volume from an image, and is a significant contribution to the broader research community with applications in urban planning, civil emergency mitigation and human population estimation.

Timing

Fall 2015 – Spring 2016

Team Outcomes

Kyle Bradbury, Raghav Saboo, Timothy L. Johnson, Jordan Malof, Arjun Devarajan, Wuming Zhang, Leslie M. Collins, Richard G. Newell. 2016. “Distributed Solar Photovoltaic Array Location and Extent Dataset for Remote Sensing Object Identification.” Scientific Data 3.

Automated Rooftop Solar PV Detection and Power Estimation through Remote Sensing (poster by Arjun Devarajan, Brody Kellish, Cassidee Kido, Aaron Newman, Nick Von Turkovich, Wuming Zhang, Kyle Bradbury, Leslie Collins, Timothy Johnson, Richard Newell)

Bradbury, K.; Brigman, B.; Collins, L.; Johnson, T.; Lin, S.; Newell, R.; Park, S.; Suresh, S.; Wiesner, H.; Xi, Y. 2016. Aerial Imagery Object Identification Dataset for Building and Road Detection, and Building Height Estimation.

Bradbury, K.; Saboo, R.; Johnson, T.; Malof, J.; Devarajan, A.; Zhang, W.; Collins, L.; Newell, R. 2016. Distributed Solar Photovoltaic Array Location and Extent Data Set for Remote Sensing Object Identification. (data set of known solar panel locations in imagery data containing over 18,000 hand-annotated locations of solar panels and their boundaries)

This Team in the News

New Dataset Developed at Duke Will Benefit Solar Energy Growth

Distinguishing Solar Panels from Swimming Pools

How Much Did That Shower Cost? The Energy Data Analytics Lab and Smart Tech Insights

See related teams Energy Data Analytics Lab (2016-2017) and The University as a Laboratory for Smart Grid Data Analytics (2014-2015).

Themes

Faculty/Staff Team Members

Kyle Bradbury, Duke University Energy Initiative*
Leslie Collins, Pratt School - Electrical & Computer Engineering*
Timothy Johnson, Nicholas School - Earth & Ocean Sciences*
Richard Newell, Nicholas School of the Environment*
Steven Palumbo, Duke Facilities Management*
Guillermo Sapiro, Pratt School - Electrical & Computer Engineering

Graduate Team Members

Yixuan Zhang, Nicholas School - Master of Environmental Mgmt.

Undergraduate Team Members

Chia Rui Chang
Arjun Devarajan, Computer Science (BS), Computer Science (BS2)
Brody Kellish, Electrical & Computer Engineering, Computer Science (BS2)
Cassidee Kido, Electrical & Computer Engineering
Ting Lu
Aaron Newman, Electrical & Computer Engineering, Computer Science (BS2)
Nicholas Von Turkovich, Electrical & Computer Engineering, Computer Science (BS2)
Wuming Zhang, Electrical & Computer Engineering

* denotes team leader

Status

Completed, Archived