How much power is a set of wind farms likely to generate over the next 24 hours? How will occupants in a commercial building interact to consume energy? Being able to answer prediction questions like these is vital to developing a more sustainable energy infrastructure: If we can predict renewable energy production and demand ahead of time, we can schedule energy resources more efficiently and reliably, leading to significant reductions in greenhouse gas emissions. Unfortunately, these are also inherently uncertain quantities we need to predict; for example, no matter how good our algorithms are, we can't predict human behavior with perfect accuracy. In order to use such predictions, we need to be able to properly model the uncertainty inherent in these domains. We need to make predictions that are not only correct on average, but which capture the complex random fluctuations and correlations between predicted quantities. Only then can we schedule energy resources in a way that accounts for these uncertainties.

This project develops and uses a recently-proposed framework for modeling --- sparse Gaussian conditional random fields --- a generalization of the commonly used Markov random field. This framework efficiently models high-dimensional distributions by exploiting sparsity in the inverse covariance matrix. The project extends the state of the art by greatly accelerating model learning, by extending existing theory to understand when these models can effectively learn high-dimensional predictors, and by generalizing the predictions to the non-Gaussian setting through copula methods. The project uses these algorithms to build forecasting models in four crucial domains in the energy sector: energy demand, wind power, user occupancy in homes and commercial buildings, and personal energy consumption from smart meters.

The project has exemplary broader impacts. First, the research deals directly with application domains crucial to efficient energy management, where even small advances can have a sizable impact on sustainability. Second, the PI leverages the research to bring the power systems and machine learning communities closer together, disseminating the results at both machine learning and power systems venues, and releasing material and video lectures to practitioners in energy. Finally, the project harnesses the research to increase diversity within STEM fields by advising under-represented minorities at the graduate and undergraduate level, and by engaging High School students and teachers with talks illustrating how computation can be used to address problems in sustainability.

Agency
National Science Foundation (NSF)
Institute
Division of Information and Intelligent Systems (IIS)
Application #
1320402
Program Officer
Todd Leen
Project Start
Project End
Budget Start
2013-08-01
Budget End
2015-07-31
Support Year
Fiscal Year
2013
Total Cost
$236,946
Indirect Cost
Name
Carnegie-Mellon University
Department
Type
DUNS #
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213