This image shows two ways of visualizing data on electricity produced by different types of power plants across the United States.

Energy systems are a foundation of the modern world -- and they're in a period of significant transition. These systems include electricity, transportation, industry, residential and commercial buildings, and energy markets. Many are connected by and produce vast quantities of data that are capable of revealing insights about performance and energy use. These insights may remain undiscovered until we translate those data and apply them in practical ways.

Duke's groundbreaking Energy Data Analytics Lab is developing and applying advanced data analytics tools to transform information from energy systems into insight that improves their reliability, resiliency, environmental sustainability, productivity and affordability.

The Energy Data Analytics Lab is a hub of research and education activity at Duke and is engaging with a broad spectrum of experts and decision-makers in the business and policy community to provide energy data-focused solutions to challenging problems in the energy space.

Download more information about the Energy Data Analytics Lab.

Lab Leadership:


Campus Data Analytics and Workshops:



Examples of individual appliance data disaggregated from aggregate building data

Electric meter data, advanced thermostats and other components of modern smart grid and systems connected in an "internet-of-things" have the potential to enable significant insights and energy automation in buildings. The Energy Data Analytics Lab is exploring methods and applications for non-intrusive load monitoring (NILM), which breaks down aggregate energy consumption data from a building’s smart electric meter to provide feedback on each type of device that is consuming energy. These techniques may allow building owners to cheaply automate building energy audits, identify energy efficiency improvements, predict equipment failure, and maximize cost savings by using less power or using it at a time when the cost is lower. Researchers including Energy Initiative founding Director Richard Newell and Lab Managing Director Kyle Bradbury are looking at ways to improve NILM techniques and applications, and how data from the growing number of connected devices in a building may be leveraged to optimize building energy consumption through automation.

Researchers: Mary Knox (Pratt Engineering), Leslie Collins (Pratt Engineering), Kyle Bradbury (Energy Initiative), Richard Newell (Energy Initiative)

Plot demonstrating the difference in productivity between conventional and unconventional wells

Horizontal drilling and hydraulic fracturing have fundamentally changed the oil and gas industry, with a significant impact on natural gas markets and pricing. Through these manufacturing processes, gas can be quickly and reliably extracted from shale formations. This suggests that natural gas production from shale wells should be more responsive to natural gas prices, which should increase the elasticity of gas supply and dampen gas price volatility. A team of Duke researchers is using a large dataset on oil and gas wells to investigate the changing nature of the gas supply curve. The data provided by DrillingInfo contains detailed information on hundreds of thousands of oil and gas wells in the United States, and tens of millions of well-month observations. The research will also be expanded to explore tight oil production.

Researchers: Brian Prest (Nicholas School of the Environment), Richard Newell (Former Director, Energy Initiative)

Demonstrates the energy-saving impact of automation (green line) over behaviorally-motivated changes

Public policy professor Matthew Harding is comparing how smart algorithms and devices may work better than trying to change individual behavior when it comes to improving energy consumption decisions. As utilities and appliance manufacturers offer advanced data on their products, organizations and individuals can use the information to increase energy efficiency and reduce costs by relying more on automated systems that respond to system signals such as weather. Harding is looking in particular at how consumers respond to incentives to improve energy conservation or adopt green power.

Researchers: Matthew Harding (Sanford Public Policy)

Example of solar array detection algorithm estimating the location of panels (yellow) in images compared to their true location (black)

Estimates of rooftop solar photovoltaic capacity and power generation are limited by government statistical collections that focus on central station power rather than distributed energy production at residential and commercial buildings. This project addresses this information gap using modern data analytics methods based on remote image recognition. Specifically, this project investigates the use satellite and aerial imagery with image processing tools to estimate the visible solar panels on rooftops across the United States. We are curating the necessary ground-truth data to train image recognition algorithms. The results will improve solar PV estimates and aid government agencies and power grid independent system operators (ISO’s) in evaluating the state of distributed PV deployment and use that information for planning purposes to increase system reliability and resilience. This project also has been a focal point for a Data+ summer experience and a Bass Connections project team.

Researchers: Jordan Malof (Pratt Engineering), Rui Hou (Pratt Engineering), Kyle Bradbury (Energy Initiative), Richard Newell (Energy Initiative), Leslie Collins (Pratt Engineering)

Water heating in the residential building sector is the second-highest energy end use after space heating. Modern water heaters come in many forms ranging from conventional storage water heaters to tankless and heat pump water heaters. Although many energy efficiency programs advocate the replacement of conventional systems with heat pump water heaters, conventional tanks still dominate the market and are likely to be around for years to come. Using state-of-the-art sensor data from industry partners, we are developing control systems to maximize the energy efficiency of these systems, evaluating the potential energy and cost savings to water heater owners.

Researchers: Mengyang Lin (Energy Initiative), Kyle Bradbury (Energy Initiative), Richard Newell (Energy Initiative)

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

Location: Student Wellness Center, Room 148
Time: 10:30 am to 12:15 pm
Location: Fitzpatrick Center, Schiciano Auditorium Side B, room 1466
Time: 2:00 pm to 3:00 pm
  • Lecturing Fellow and Managing Director, Energy Data Analytics Lab

    Kyle brings experience in machine learning and statistical modeling to energy problems.