15-830/15-630, Fall 2012:
Computational Methods in Sustainable Energy

Course Info

Number: 15-830 (PhD), 15-630 (MS)
Time: TR 10:30-11:50 (starts Sept 11)
Classroom: Wean 4709 (changed from NSH 1305!)
Instructor: J. Zico Kolter
Units: 12
Syllabus: Available here (updated 8/28)

Course Description

Sustainable energy poses one of the largest challenges facing the world. Although energy topics span many disciplines, ranging from material sciences to public policy, it is becoming apparent that computational techniques such as simulation, prediction, optimization, and control have the potential to drastically impact virtually all of these areas. This course provides an introduction to recent advances in computational methods applied to sustainable energy domains; the goal is to provide students with a broad background in state-of-the-art computational methods that repeatedly arise in these domains, such as machine learning, optimization, and control, and to provide hands-on experience applying these methods to real-world domains. In particular, much of the class will use real data from the Pennsylvania electrical grid as a running example, and address issues regarding the prediction, modeling, and control of electricity from existing and renewable energy sources. Although listed in Computer Science, the course is expected to be of interest to students in many departments, including ECE, MechE, CEE, and EPP.

This course is offered in MS and PhD levels, under course numbers 15-630 and 15-830 respectively. Lectures are the same, but the PhD version will have additional problems and will require an original research final project; the MS level course may have a literature survey for the final project.

Application areas include:
  • electricity demand and renewable resource prediction
  • modeling energy consumption in buildings
  • electrical power systems, power flow, and power markets
  • control of distributed storage
Computational techniques include:
  • regression and classification
  • time series prediction
  • Newton's method for non-linear equations
  • convex optimization
  • model predictive control
  • biweekly problem sets and a written final project
  • some programming experience (assignment use MATLAB)
  • basic background in linear algebra