Good morning EECS Students!
Professor Xiangqi Zhu will be teaching Data Science for Contemporary Power Grid this fall term. The course syllabus is attached and the description is below. If you have any questions,
please do not hesitate to contact Dr. Zhu.
Course Description:
This course introduces the principles and applications of data science in the context of modern
power grids. We will cover various topics including statistical analysis, predictive analytics, data
visualization, supervise/unsupervised learning, and how these techniques can be contributed to
reliable, resilient, and safe power grid operations.
Learning Objectives:
By the end of this course, students will be able to apply principles and techniques of data science
to analyze, interpret, visualize, and utilize complex datasets from contemporary power grids. They
will develop the skills to implement statistical analysis, predictive analytics, and machine learning
algorithms to address challenges in power grid operations, improving grid reliability and resilience.
Moreover, students will gain an understanding of the social impact associated with applying data
science in the context of power grid management. Specifically, 5 goals are set for students to
achieve in this course:
1. Analyze power grid data: Effectively identify patterns and anomalies in the data.
2. Predict power grid data: Effectively estimate power grid generation, demand, and
behavior.
3. Visualize power grid data: Effectively communicate insights and findings using data
visualization techniques.
4. Evaluate social impact: Effectively evaluate social impact of the data science applications
in power grid, including privacy, equity, and transparency in data-driven decision-making
processes.
Sherry Barrett
Program
& Assessment Assistant
Research Center Program Assistant
School of Electrical Engineering & Computer Science
Oregon State University
|
1148 Kelley Engineering Center
Office Phone: 541-737-5556