Lectures: Hewlett Room 102, Tuesdays, 4:15-5:05PM
Coordinators: Daniel C. O’Neill, Packard 223, (650) 575-1367, email@example.com and Dimitry Gorinevsky, Packard 233, (650) 724-6783, firstname.lastname@example.org
Office hours: Tuesdays, 3 pm to 4 pm, Packard 223, and Thursdays, 3 pm to 4 pm, Packard 233,
Textbook and optional references: There is no textbook. Lecture notes will be available in Adobe acrobat (pdf) from the class web page, www.stanford.edu/class/ee392n.
Homework: This is a seminar course and no homework will be assigned.
Prerequisites: (helpful but not mandatory) Stat 116; EE263 or Eng 207a; basic communications
Catalog description: The course will focus on Data Science techniques in modern energy systems and on the infrastructure required to support such techniques. The main focus will be on Big Data applications. The goal of the course is to prepare the students for careers in energy related areas by teaching systems engineering perspective. The course will discuss analytics for monitoring of the power generation systems, power transmission and distribution systems, asset management, and energy use in buildings. The examples and case studies illustrating the analytics functions and information systems in energy will be presented by prominent guest lecturers from industry.