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.
Acknowledgements: This class draws from a number of sources, including Prof. Yang Weng’s class at ASU, Prof. Hao Zhu’s class at UT Austin, Prof. Ned Mohan’s classes at University of Minnesota.

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

Class: Tuesdays, 6pm-8:50pm, ECE Building, room 403. Zoom link https://washington.zoom.us/j/94913728987
Instructor: Baosen Zhang, zhangbao@uw.edu, Office: EEB M310, Office Hour: Thursday 3:30 to 4:30 https://washington.zoom.us/j/97861129780 or by request
TAs: Daniel Tabas, Office: EEB 203, Office Hour: Wednesday 5-7pm, https://washington.zoom.us/j/99009774415, dtabas@uw.edu;
Notebook: The main notebook we use for programming is here

Note:

  1. Lectures will be hybrid. The class will be recorded. You are encouraged to participate either in person or online in real-time. Just listening to the recordings wouldn’t be as useful.
  2. I will try to respond to emails within 24 hours. Remember, the TA often reply much faster than I do.
  3. It is very important to do the programming assignments. Daniel 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
  6. IEEE PES Subcommittee on Big Data \& Data Analytics

Detailed Topics:

  1. Introduction, review, slides, video
  2. Regression 1, slides, video
  3. Regression 2, slides, video
  4. Forecasting, slides, video
  5. Classification 1, slides, video
  6. Classification 2, slides, video
  7. Classification 3, slides, video
  8. Deep Neural Networks (Daniel’s Lecture), slides, video
  9. Clustering, Storage Operation Example, slides, video
  10. Project Presentation

Homework and Project:

  1. There are weekly homework assignments.
  2. Final Project presentation on May 31st, via zoom. Project report due June 10th (please keep it to no more than 10 pages).

Homework Assignments:

  1. Homework 1, due April 9th, 11:59pm. Submit written answers to CANVAS. For programming questions, see notebook. Solutions
  2. Homework 2, due April 16th, 11:59pm. Submit written answers to CANVAS 1 and your saved notebook for programming questions to CANVAS 2. For the programming questions, see notebook. Solutions
  3. Homework 3, due April 23rd, 11:59pm. Submit written answers to CANVAS 1 and your saved notebook for programming questions to CANVAS 2. For the programming questions, see notebook. Solutions
  4. Homework 4, due April 30th, 11:59pm. Submit written answers to CANVAS 1 and your saved notebook for programming questions to CANVAS 2. For the programming questions, see notebook
  5. Homework 5, due May 9th, 11:59pm. Submit written answers to CANVAS 1 and your saved notebook for programming questions to CANVAS 2. For the programming questions, see notebook Solutions
  6. Homework 6, due May 16th, 11:59pm. Submit answers to CANVAS. For the programming questions, see notebook
  7. Homework 7, due May 27th, 11:59pm. Submit answers to CANVAS. For the programming questions, see notebook