Smart Meter Data Analysis

Machine learning for improving building energy efficiency

Plot of energy consumption in an example household versus the time of day. Individual appliance data is much more informative than aggregate building data.

For decades, only monthly electricity data was collected to provide a bill for building electricity consumption. However, the presence of smart meters has opened the door to huge quantities of data at sampling intervals ranging from 15 minutes down to 1 second. This project investigates techniques for decomposing the aggregate energy consumption from a building’s smart electric utility meters to provide device-level feedback (e.g. refrigerator, dishwasher, computer) and the time at which energy is consumed to improve building energy efficiency and operation.

Collaborators: Bohao Huang, Mary Knox, and Leslie Collins.

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Energy Infrastructure Analysis using Remote Sensing Data

Using satellite imagery to better understand and plan energy systems globally

Energy systems are rapidly evolving in both the developed and developing world. Distributed energy resources such as solar photovoltaics are experiencing significant growth, but are not typically controlled or monitored by independent system operators of the grid. At the same time, one in five people around the world still lack access to modern electricity, and identifying optimal pathways to electrification requires detailed knowledge of local infrastructure and the precise locations of communities that would benefit most from electrification. Here, we work to fill these important information gaps by analyzing satellite imagery and other remotely sensed data using machine learning techniques, specifically through convolutional neural networks.

Using satellite imagery and machine learning, we are developing techniques to:

...Identify the location and capacity of solar photovoltaic arrays in the United States:

...Assess energy access in developing nations:

...Estimate building energy consumption:

...and map grid infrastructure including transmission and distribution lines to determine pathways for electrification in developing countries:

Collaborators: Jordan Malof (Pratt School of Engineering), Bohao Huang (Pratt School of Engineering), Wei (Wayne) Hu (Energy Initiative), Leslie Collins (Pratt School of Engineering), Marc Jeuland (Sanford School of Public Policy), Bryan Bollinger (Fuqua School of Business), Steve Sexton (Sanford School of Public Policy), T. Robert Fetter (Nicholas Institute for Environmental Policy Solutions), Tim Johnson (Nicholas School of the Environment), Artem Streltsov (formerly Energy Initiative)

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Energy Storage

Evaluating the arbitrage potential operation of energy storage systems in power markets

Figure showing a simulated energy storage device charge and discharge based on the real-time price of electricity

Energy storage systems (ESSs) can increase power system stability and efficiency, and facilitate integration of intermittent renewable energy, but deployment of ESSs will remain limited until they achieve an attractive internal rate of return (IRR). Linear optimization is used to find the ESS power and energy capacities that maximize the IRR when used to arbitrage 2008 electricity prices (the highest of the past decade) in seven real-time markets in the United States for 14 different ESS technologies.

Our results show that the profit-maximizing size (i.e. hours of energy storage) of an ESS is primarily determined by its technological characteristics (round-trip charge/discharge efficiency and self-discharge) and not market price volatility, which instead increases IRR. Most ESSs examined have an optimal size of 1–4 h of energy storage, though for pumped hydro and compressed air systems this size is 7–8 h. The latter ESSs already achieve IRRs >10%, but could be made even more profitable with minimal cost-reductions by reducing power capacity costs. The opposite holds for flywheels, electrical ESSs (e.g., capacitors) and a number of chemical ESSs (e.g., lead acid batteries). These could be made more profitable with minimal cost-reductions by reducing energy capacity costs.

Collaborators: Lincoln Pratson, Dalia Patino-Echeverri

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Wind & Solar Integration

Comparing methods for integrating variable wind and solar energy systems into the grid

Intermittent energy resources, including wind and solar power, continue to be rapidly added to the generation fleet domestically and abroad. The variable power of these resources introduces new levels of stochasticity into electric interconnections that must be continuously balanced to maintain system reliability. Energy storage systems (ESSs) offer one potential option to compensate for the intermittency of renewables. Geospatially diverse siting of wind and solar, to make use of uncorrelated wind speeds and solar insolation, offer an alternative method for reducing the variability of renewable energy. The aim of this study is to explore the cost and reliability tradeoffs associated with using energy storage versus using geographically diverse siting for wind and solar integration.

Collaborators: Hyeongyul Roh, Artem Streltsov, Lincoln Pratson, Dalia Patino-Echeverri, and Xiaochen Sun

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Bass Connections Team

Interdisciplinary student research project teams

Photo from the Pratt School of Engineering: "Data and the Duke Engineer"

Bass Connections is university-wide program for students to engage in interdisciplinary problem-based project teams. These students of multiple educational-levels spend a year working for credit on a team-based project guided by Duke faculty and staff.

Since 2013, I have led Bass Connections student research teams through projects to better operate or understand energy systems by using machine learning tools, resulting in the publication of datasets, award-winning student posters, and a generally a great team of students eager to take on the challenging energy problems of our world. Below are links to past project teams:

Energy infrastructure map of the world: mapping transmission and distribution lines from satellite imagery

Estimating electricity access in developing countries from satellite imagery

Predicting building energy consumption from satellite imagery

Identifying and estimating the capacity of solar photovoltaic arrays from satellite imagery

Designing and implementing an energy disaggregation system

The University as an energy laboratory

Collaborators: Jordan Malof, Leslie Collins, Timothy Johnson, Richard Newell

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Data+

Student research mentorship program

Data+ is a ten-week summer research experience organized by the Information Initiative at Duke that welcomes Duke undergraduates and graduate student leaders. Students join small project teams, working alongside other teams in a communal environment. They learn how to marshal, analyze, and visualize data, while gaining broad exposure to the modern world of data science.

I have led a number of past project (listed below) that give students the experience of processing data to prepare for use in machine learning and through a series of training sessions help them to become familiar with the precepts of machine learning to apply to the data they curate through this summer research experience.

Energy infrastructure map of the world

Electricity access in developing countries from aerial imagery

Energy resource assessment from satellite imagery

Solar power estimation from satellite imagery

Collaborators: Jordan Malof, Timothy Johnson, Leslie Collins, Jordan Malof, Robyn Meeks, Rob Fetter, Marc Jeuland, Guillermo Sapiro, Paul Bendich.

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Python Programming

Fundamentals of Python programming for data science applications

Course example of the Python ecosystem

Course Materials

Data science is like painting. Your canvas is your dataset, your statistical techniques are your colors, and your technique and artistry comes through your programming. Data scientists with strong programming skills are able to quickly implement techniques and are more readily able to be more creative problem solvers and scientists. This online course is designed to will guide students through the basics of programming with Python with an eye towards the most important components needed to work with data. This course begins with an overview of the Python ecosystem and goes on to cover data types, operators, flow control, functions, modules, packages, objects, and classes. Since this course is data science focused, there is also a unit on computational programming that covers numpy, matplotlib (for plotting), and pandas (for working with data frames). There is also a discussion of best practices for scientific computing.

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Machine Learning Course

Introduction to machine learning for data science

Example lecture slide

Course Materials

In almost every field, there is a need to draw inference from or make decisions based on data. The goal of this course is to provide an introduction to machine learning that is approachable to diverse disciplines and empowers students to become proficient in the foundational concepts and tools while working with interdisciplinary real-world data. You will learn to (a) structure a machine learning problem, (b) determine which algorithmic tools are applicable to a given problem, (c) apply those algorithmic tools to diverse, interdisciplinary data examples, (d) evaluate the performance of your solution, and (e) how to accurately interpret and communicate your results. This course is a fast-paced, applied introduction to machine learning that arms you with the basic skills you will need in practice to both conduct analyses and effectively communicate your results.

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