Energy Data Analytics Projects
This project is also part of Duke’s first Climate+ cohort. A team of students led by researchers at Duke and abroad will develop and evaluate machine learning solutions to model behavioral patterns of electric use, emphasizing data privacy. Data collected in different parts of the world will be analyzed to...
Data+ students led by Prof. Henri Gavin will develop AI models for on-site earthquake early warning, in which sensors at a site provide warnings at that site. The Data+ project will integrate into ongoing work on geophone sensors, IOT microcontrollers, and networking. The Data+ team will focus on machine learning...
This project is also part of Duke’s first Climate+ cohort. A student team working with the Energy Data Analytics Lab will work to democratize access to data relevant to climate change mitigation and adaptation planning as well as the underlying models to acquire those data. This project will work towards building the...
Sharrin Manor, Arjun Devarajan, Wuming 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. Project Results The students coded their own proof-of-principle algorithm which identified...
Caroline Tang (Math/Stats) joined CS majors Frankie Willard and Alex Kumar in a ten-week exploration of AI methods to improve the mapping of energy infrastructure within satellite imagery. The team used cutting-edge methods to create synthetic imagery that, when blended with real imagery, improved the performance of deep learning methods...
Boning Li (Masters Electrical and Computer Engineering), Ben Brigman (Electrical and Computer Engineering), Gouttham Chandrasekar (Electrical and Computer Engineering), Shamikh Hossain (Computer Science, Economics), and Trishul Nagenalli (Electrical and Computer Engineering, Computer Science) spent ten weeks creating datasets of electricity access indicators that can be used to train a classifier to detect electrified villages. This coming academic year, a Bass...
Tejvasi Patil (MEM), Sophia Stameson (CS), and Larry Zheng (Bio) spent ten weeks working with drone footage from different rainforest sources. The team designed a pipeline that performed image classification on the drone footage, and curated a training dataset using SQL. View the team’s project poster here Watch the...
Intelligent mobile sensor agent can adapt to heterogeneous environmental conditions, to achieve the optimal performance, such as demining, maneuvering target tracking. The mobile sensor agent is a robot with onboard sensors, and it is deployed to navigate obstacle-populated workspaces subject to sensing objectives. The expected performance of available future measurements is...
We trained an object detection model to locate wind turbines in overhead satellite imagery. Because these deep learning models require large amounts of training data, and satellite imagery of wind turbines is rare and expensive to collect, we created synthetic satellite imagery using 3D modeling software. We then supplemented our...
Between 1935 and 1945, rural electricity access shot up from roughly 10% to 90%. During this time, the Rural Electrification Administration funded an Electric Farm Equipment (EFE) Roadshow as part of its mission to expand electricity access and demand. Digitizing massive amounts of archival data, our team has sought to...
Cassandra Turk (Economics) and Alec Ashforth (Economics, Math) spent ten weeks building tools to help minimize the risk of trading electricity on the wholesale energy market. The team combined data from many sources and employed a variety of outlier-detection methods and other statistical tools in order to create a large dataset of extreme...
Brooke Erikson (Economics/Computer Science), Alejandro Ortega (Math), and Jade Wu (Computer Science) spent ten weeks developing open-source tools for automatic document categorization, PDF table extraction, and data identification. Their motivating application was provided by Power for All’s Platform for Energy Access Knowledge, and they frequently collaborated with professionals from that organization. Click here to read the...
Varun Nair (Mechanical Engineering), Tamasha Pathirathna (Computer Science), Xiaolan You (Computer Science/Statistics), and Qiwei Han (Chemistry) spent ten weeks creating a ground-truthed dataset of electricity infrastructure that can be used to automatically map the transmission and distribution components of the electric power grid. This is the first publicly available dataset of its kind, and will be analyzed...
Aaron Chai (Computer Sciece, Math) and Victoria Worsham (Economics, Math) spent ten weeks building tools to understand characteristics of successful oil and gas licenses in the North Sea. The team used data-scraping, merging, and OCR method to create a dataset containing license information and work obligations, and they also produced ArcGIS visualizations of...
Yueru Li (Math) and Jiacheng Fan (Economics, Finance) spent ten weeks investigating abnormal behavior by companies bidding for oil and gas rights in the Gulf of Mexico. Working with data provided by the Bureau of Ocean Energy Management and ExxonMobil, the team used outlier detection methods to automate the flagging of abnormal behavior,...
Varun Nair (Economics, Physics), Paul Rhee (Computer Science), Jichen Yang (Computer Science, ECE), and Fanjie Kong (Computer Vision) spent ten weeks helping to adapt deep learning techniques to inform energy access decisions. Click here to read the Executive Summary Faculty Lead: Kyle Bradbury Project Manager: Fanjie Kong
Yoav Kargon (Mechanical Engineering) and Tommy Lin (Chemistry, Computer Science) spent ten weeks working with data from the Water Quality Portal (WQP), a large national dataset of water quality measurements aggregated by the USGS and EPA. The team went all the way from raw data to the production of Pondr, an interactive and...
Marco Gonazales Blancas (Civil Engineering) and Mengjie Xiu (Masters, BioStatistics) spent ten weeks building tools to help Duke reduce its energy footprint and achieve carbon neutrality by 2024. The team processed and analyzed troves of utility consumption data and then created practical monthly energy use reports for each school at Duke. These reports...
Yanchen Ou (Computer Science) and Jiwoo Song (Chemistry, Mechanical Engineering) spent ten weeks building tools to assist in the analysis of smart meter data. Working with a large dataset of transformer and household data from the Kyrgyz Republic, the team built a data preprocessing pipeline and then used unsupervised machine-learning techniques to assess...
Vincent Wang (Computer Science, CE), Karen Jin (Bio/Stats), and Katherine Cottrell (Computer Science) spent ten weeks building tools to educate the public about lake dynamics and ecosystem health. Using data collected over a period of 50 years at the Experimental Lake Area (ELA) in Ontario, the team preprocessed and merged datasets, made a...
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