Text: 2020 Energy Data Analytics Symposium. Videos now streaming.

How can machine learning and data science tools help us better understand energy systems and manage them to be more accessible, affordable, reliable, and clean? 
Experts and emerging scholars highlighted cutting-edge approaches to this question at the Energy Data Analytics Symposium in December 2020. More than 200 students, researchers, and industry professionals from nearly 100 organizations and 19 countries took part in the two-day convening. The event was organized by the Energy Data Analytics Lab at Duke University and funded by a grant from the Alfred P. Sloan Foundation.

Check out 2020 Energy Data Analytics Symposium videos below. The 36 presentations include a keynote address by  Dr. Colin Parris  (Senior Vice President and Chief Technology Officer, GE Digital); four topical sessions with insights from industry and academic experts; and 5-minute "lightning talks" by emerging scholars. You can also see our list of five key takeaways from the Symposium.

Dr. Brian Murray, Duke University 
Director, Energy Initiative 

Dr. Evan Michelson, Alfred P. Sloan Foundation 
Program Director, Energy and Environment 

Dr. Colin Parris, GE Digital 
Senior Vice President and Chief Technology Officer 

2020 Energy Data Analytics Symposium Keynote Presentation 

Dr. Colin Parris leads teams that work to leverage technologies and capabilities across GE to accelerate business impact and create scale advantage for digital transformation. Dr. Parris’s and his team’s work in artificial intelligence extends to the energy, aviation, and healthcare sectors. He created and leads the Digital Twin Initiative across GE. Dr. Parris has a Ph.D. in electrical engineering from the University of California, Berkeley.

Presentation: Automatically Estimating Distributed Solar Photovoltaic Capacity and Building Energy Consumption Using Satellite Imagery 

Dr. Kyle Bradbury, Duke University 
Managing Director, Energy Data Analytics Lab  
Assistant Research Professor, Electrical and Computer Engineering  

Dr. Kyle Bradbury leads applied research projects at the intersection of machine learning techniques and energy problems. His work includes satellite imagery-based energy object identification, smart meter data analysis and disaggregation, and the cost/reliability tradeoffs of wind and solar integration into the grid through energy storage. He has a Ph.D. from Duke University focused on energy systems modeling. 
 

Presentation: Development Seed's Work Using Machine Learning to Detect Energy 

Martha Morrissey, Development Seed 
Machine Learning Engineer  

Martha Morrissey applies machine learning models to detect urban infrastructure like uncommon building types and energy grids faster and more efficiently. She is passionate about bringing open geospatial data to academics and innovators as a basis for addressing large-scale global issues. Morrissey previously worked at Maxar Technologies (formerly DigitalGlobe) where she helped on API development for machine learning training data access, model creation, and model sharing. She has a master's degree in geography from the University of Colorado, Boulder. 
 

Presentation: Estimating Greenhouse Gas Emissions from Every Power Plant on the Planet 

Dr. Heather Couture, Pixel Scientia Labs 
Founder, Pixel Scientia Labs   
Machine Learning Consultant & Researcher  

Dr. Heather Couture is the founder of the machine learning consulting firm Pixel Scientia Labs, which guides research and development teams to fight cancer and climate change more successfully with AI. She is currently working with WattTime on their Climate TRACE project for the electricity sector. She has a Ph.D. in computer science from the University of North Carolina at Chapel Hill.  

Presentation: Surrogate Modeling for Scalable Assessment of Wind Plant Technology Innovation 

Dr. Dylan Harrison-Atlas, National Renewable Energy Laboratory 
Senior Data Scientist, Geospatial Data Science Group, Strategic Energy Analysis Center 

Dr. Dylan Harrison-Atlas focuses on national scale assessment of energy problems and solutions using geospatial and machine learning methods. Active areas of research include investigating land use implications at the forefront of the energy-environment nexus and developing novel approaches to modeling technology innovation benefits across broad geographic extents. Dr. Harrison-Atlas has a Ph.D. in ecology from Colorado State University.  
 

Presentation: Applying Data Science to Utility Operations 

John Pressley, Duke Energy Corporation 
Director, Information Management Solutions, Digital Transformation 

John Pressley works with Duke Energy’s Digital Transformation team, where he cut his teeth on scaling and supporting the Information Management Architecture (IMA). His portfolio includes over 32 products and platforms focused on pushing the company in new, innovative directions. He previously worked with Duke Energy’s HR IT team, as an analyst at Accenture in the High Tech and Telecomm division – with clients including Texas Instruments, Sony Erickson, Verizon, and Shell Oil, and Wachovia. Pressley has an M.B.A. from Wake Forest University.  
 

Dylan Lustig, Duke Energy Corporation 
Lead Analyst, Digital Transformation 

Dylan Lustig works with Duke Energy’s Digital Transformation team. He works as a portfolio lead within Digital Transformation’s Information Management Solutions group and supports dozens of products and product teams across many domains of the company. With Duke Energy since 2016, he has also championed the adoption of new, Agile ways of working within IT, worked on the initial development of the company’s Internet of Things (IoT) platform, and worked in the regulatory and corporate finance space.  

Presentation: Infrastructure Constraints on Energy Transitions: Lessons from History 

Dr. Elisabeth Moyer, University of Chicago  
Associate Professor, Geophysical Sciences 

Director, Center for Robust Decision-making on Climate and Energy Policy (RDCEP)  
Dr. Moyer is an Associate Professor at the University of Chicago Department of the Geophysical Sciences and the director of the university's Center for Robust Decision-making on Climate and Energy Policy (RDCEP). Dr. Moyer’s research includes climate response to greenhouse-gas forcing; development of tools for impacts assessment; statistical emulation of climate model output; and climate and energy policy evaluation. Dr. Moyer has a Ph.D. in planetary science from the California Institute of Technology, and undergraduate degrees in physics and anthropology from Stanford University. 

Presentation: Harnessing Opportunistic Data and Physics Intuition to Enable Autonomous Buildings 

Dr. Mario Bergés, Carnegie Mellon University 
Professor, Civil, and Environmental Engineering 
Director, Intelligent Infrastructure Research Lab 
 

Dr. Mario Bergés is interested in making our built environment more operationally efficient and robust through the use of information and communication technologies, so that it can better deal with future resource constraints and a changing environment. His work largely focuses on developing approximate inference techniques to extract useful information from sensor data coming from civil infrastructure systems, with a particular focus on buildings and energy efficiency. Dr. Bergés has a Ph.D. in civil and environmental engineering from CMU. 

Presentation: Counterfactual Prediction Methods as a Substitute for Randomized Controlled Trials: Evidence from High-Frequency Energy Data 

Dr. Brian Prest, Resources for the Future 
Economist 

Dr. Brian Prest uses economic theory and econometric models to improve energy and environmental policies by assessing their impacts on markets and pollution. This includes work on the impacts of federal tax credits for coal use, the social cost of carbon, econometric analysis of the oil and gas industry, analysis on the economic effects of rising temperatures, and household responses to time-varying electricity pricing. Dr. Prest has a Ph.D. in environmental and resource economics from Duke University. 
 

Presentation: Decomposing 'the' Elasticity of Demand: Empirical and Policy Insights from 300 Million Natural Gas Bills 

Dr. Edward Rubin, University of Oregon  
Assistant Professor, Economics

Dr. Ed Rubin's research focuses on environmental/energy economics and inequality—particularly policy impacts, strategic responses to regulation, and measuring exposure/access. He is especially interested in combining causally informed econometrics/statistics with data science (machine learning, spatial analysis) to shed new light on previously resolved or unanswered questions. He received his Ph.D. in agricultural and resource economics from the University of California, Berkeley. 

Presentation: CityDNN: A Deep Neural Network for Urban Energy Simulation Models 

Dr. Zoltan Nagy, University of Texas at Austin 
Assistant Professor,  Civil, Architectural, and Environmental Engineering 
Director, Intelligent Environments Laboratory

Dr. Zoltan Nagy is a roboticist turned building engineer, his research interests are in smart buildings and cities, renewable energy systems, control systems for zero emission building operation, machine learning and artificial intelligence for the built environment, complex fenestration systems and the influence of building occupants on energy performance. Dr. Nagy received a Ph.D. in robotics from ETH Zurich, Switzerland.

Presentation: Energy Access Explorer: A Mapping Platform to Connect SDG 7 and Sustainable Development Outcomes 

Dr. Dimitris Mentis, World Resources Institute 
Senior Energy Geographer, Energy Access 
Project Lead, Energy Access Explorer

Dr. Dimitris Mentis leads WRI’s energy access mapping efforts to prioritize areas where access to energy should be expanded while ensuring socio-economic development. He also founded and led the development of the Open Source Spatial Electrification Toolkit (OnSSET) in collaboration with multiple intergovernmental organizations, mapped the technical potential of renewable energy resources nationally and regionally through complex GIS assessments and focused on assessing the water-energy nexus at different scales. Dr. Mentis has a Ph.D. in energy and environmental systems from KTH Royal Institute of Technology, Sweden.  

Presentation: Recent Work on Matching Methods for Causal Inference from Duke's Almost-Matching-Exactly Lab 

Dr. Cynthia Rudin, Duke University  
Professor, Computer Science  
Principal Investigator, Prediction Analysis Lab

Dr. Cynthia Rudin is a Professor of Computer Science, Electrical and Computer Engineering, and Statistical Science at Duke University and directs the Prediction Analysis Lab, whose main focus is interpretable machine learning, including applications to energy systems. Dr. Rudin is also an Associate Director of the Statistical and Applied Mathematical Sciences Institute (SAMSI). She has a Ph.D. in applied and computational mathematics from Princeton University. 

Presentation: Using Night Lights to Study the Politics of Energy Access and Reliability 

Dr. Brian Min, University of Michigan  
Associate Professor, Political Science  
Faculty Affiliate for Energy Institute

Dr. Brian Min is an Associate Professor of Political Science at the University of Michigan, Ann Arbor. Dr. Min studies the political economy of development with an emphasis on distributive politics, public goods provision, and energy politics. His research uses high-resolution satellite imagery to study the distribution of electricity, including collaboration with the World Bank to monitor electricity access in India and Africa. Dr. Min has a Ph.D. in political science from UCLA. 

These five-minute presentations by emerging scholars span a wide array of energy data analytics topics. They were submitted for a competition that attracted 21 entries from 12 universities and organizations.

Judges assessed these “lightning talks” on 1) compelling communication of the core ideas and outcomes of the project to an interdisciplinary audience; and 2) innovation and potential for impact of the energy application and data science methodology. The top three presenters received monetary prizes.  

Priya Donti  

Ph.D. Student, Carnegie Mellon University

First Prize ($500): “Inverse Optimal Power Flow: Assessing the Vulnerability of Grid Data” 

Akansha Singh Bansal

Ph.D. Student, University of Massachusetts Amherst

Second Prize ($250): "See the Light: Modeling Solar Performance using Multispectral Satellite Data”

Tongshu Zheng

Ph.D. Student, Duke University

Third Prize ($100): “Estimating Solar PV Soiling Using a Satellite-Based Remote Sensing Approach”

McKenna Peplinksi

Ph.D. Student, University of Southern California

Honorable Mention:  “Predicting Changes in Southern California's Residential Electricity Consumption using Machine Learning Models.”

Noman Bashir

Ph.D. Student, University of Massachusetts Amherst

Honorable Mention: “Solar-TK: A Data-driven Toolkit for Solar PV Performance Modeling and Forecasting”

Alina Barnett

Ph.D. Student, Duke University

“Estimating Insulation Efficiency from Satellite Imagery using Super-Resolution”

Dipendra Bhattarai

Ph.D. Student, University of Tasmania, Australia

”Systematic Review on relation between Electricity Consumption and Night-time Lights”

Dr. Dong Chen

Faculty, Florida International University

“SolarFinder: Automatic Detection of Solar Photovoltaic Arrays”

Santiago Correa

Ph.D. Student, University of Massachusetts Amherst

“GridMapper: Mapping Electricity Grid Infrastructure in Developing Countries”

Jan Drgona

Data, Scientist Pacific Northwest National Laboratory

“Combining Deep Learning and Physics for Cutting  the Costs of Advanced Building Control”

Juliana Felkner

Faculty, University of Texas at Austin

“Understanding the Public Attitudes towards the Clean Power Plan Using Natural Language Processing and Deep Learning”

Dr. Todd Gerarden

Faculty, Cornell University

“Understanding the Public Attitudes towards the Clean Power Plan Using Natural Language Processing and Deep Learning”

Bohao Huang

Ph.D. Student, Duke University

“Solar Array Mapping in San Diego”

Qingran Li

Ph.D. Student, Duke University

“Disaggregating the Behavioral Factors in Residential Load Profiles”

Sayak Mukherjee

Postdoctoral Fellow, Pacific Northwest National Laboratory

"Toward Data-Driven Control of Distributed Energy Resources using Reinforcement Learning" 

Olukunle Owolabi

Ph.D. Student, Tufts University

“Data-driven Application of Modern Portfolio Theory Risk Metrics of the Electrical Grid”

Gopinath Rajendiran

Central Scientific Instruments Organisation, India

"Non-intrusive Load Monitoring (NILM) Technique Using Machine Learning Algorithms: Challenges and its Potential Applications towards Smart Sustainable Cities Development"

Jun Shepard

Ph.D. Student, Duke University

"Energy Flows through the Global Economy: 
From Production Source to Consumption Sink"

Ashwin Shirsat

Ph.D. Student, North Carolina State University

“Quantifying Residential Demand Response Potential Using Mixture Density Recurrent Neural Network”

Edgar Virguez

Ph.D. Student, Duke University

“Utility-Scale Photovoltaics plus Storage: A Cost Effective Alternative for Decarbonization?”

Tianyu Wang

Ph.D. Student, Duke University

“Power Plant Pollution Monitoring via Bandit Algorithms”

Alfred P. Sloan Foundation LogoThe symposium was organized by Dr. Kyle Bradbury, Dr. Jordan MalofDr. Brian MurrayDr. Billy Pizer, and Dr. Cynthia Rudin at Duke University's Energy Data Analytics Lab, a collaborative effort of the Duke University Energy Initiative (which houses it), the Rhodes Information Initiative at Duke, and the Social Science Research Institute. Funding support for the workshop is provided by a grant from the Alfred P. Sloan Foundation.

Note: Conclusions reached or positions taken by researchers or other grantees represent the views of the grantees themselves and not those of the Alfred P. Sloan Foundation or its trustees, officers, or staff.

Mailing Address

Duke University Energy Initiative
Box 90467
Durham, NC 27708

Street / Delivery Address

Duke University Energy Initiative
140 Science Drive
Gross Hall, Suite 101
Durham, NC 27708

919-613-1305 

Apr
15
Location: Virtual
Time: 5:00 pm to 4:00 pm
Apr
19
Location: Virtual
Time: 9:00 am to 4:00 pm