Class Syllabus:

Graduate level class on using data science tools in power systems. This course explores how data is generated in power systems and how are new technologies impacting the amount and quality of datasets, understanding popular data processing and analytic techniques, implementing existing packages to solve problems, using machine learning methods to answer questions about power system operations, and choosing appropriate methods based on objectives and datasets. See syllabus. Acknowledgement: much of this class is based on discussion with Prof. Yang Weng at ASU and his class on machine learning and power systems (EEE 598).

This website is used for most of the materials posted in this class. The Canvas Site is only used for grading.

Class: Wednesday, 6pm-8:50pm, EEB 003
Instructor: Baosen Zhang,, Office: EEB M310, Office Hour: by request
TAs: Chase Dowling, Office: EEB 215, Office Hour: TBD,; Notebook: The main notebook we use for programming is


  1. Most of the lectures will be done via the board. There are slides associated with each class, but they may not include all of the material covered on the board. It is your responsibility to make sure that you take down sufficient notes in class.
  2. I will try to respond to emails within 24 hours. Remember, the TAs often reply much faster than I do.
  3. It is very important to do the programming assignments. Chase will be around after each lecture to help.

Useful Data Sources:

  1. NREL Wind Integration Study, Solar Integration Study
  2. PECAN Street (need to register, free with a .edu email)
  3. ERCOT (Texas) load data
  4. Residential P,Q,V measurements: PQube
  5. WECC Interconnection Models, these require familiarity with power system modeling to use

Detailed Topics:

  1. Review of probability and power systems, Slides
  2. Regression 1, Slides
  3. Regression 2, State Estimation, Slides
  4. Regression 3, Forecasting Slides
  5. Forecasting 2, Classification 1 Slides
  6. Classification 2, Slides
  7. Classification 3, Slides
  8. Classification 4, Clustering Slides
  9. Clustering 2 Slides
  10. Reinforcement Learning
  11. Project Presentation

Homework and Project:

  1. There are weekly homework assignments.
  2. Final Project: team of two students, presentation in the last week of class.


Upload your project report here, due June 17th at 11pm.

Homework Assignments:

  1. Homework 1, Solution
  2. Homework 2, Solution
  3. Homework 3, due April 25, 11:00 pm. Submit at this link
  4. Homework 4, due May 2nd, 11:00 pm. Submit at this link
  5. Homework 5, due May 11th, 11:00 pm. Submit at this link
  6. Homework 6, due May 18th, 11:00pm. Submit at this link
  7. Homework 7, due May 25th, 11:00pm. Submit at this link
  8. Homework 8, due June 2nd, 11:00pm. Submit at this link