ECE 5235

ECE 5235

Course information provided by the Courses of Study 2022-2023.

The course focuses on how to transition from legacy energy delivery infrastructures dependent on fossil fuel to a sustainable decarbonized grid that harnesses distributed renewable energy resources and responsive demand from buildings, electrified transportation systems, and industrial loads. The content includes models and abstractions for the architecture of the cyber-physical energy system, its economics, and future evolution, and numerical optimization and learning methods in support of the infrastructure's safety critical operations in the legacy system and in the future architecture. At the MSc level the students will focus on learning how to use tools and data while at the graduate level the students will be asked to also solve problems, formulate novel solutions, interpret results. Similarly, to differentiate the MSc from PhD level and course outcomes, the final project will require the MSc students to pick one out of a set of predefined problems while the PhD students will have to define an original problem and solution.

When Offered Spring.

Prerequisites/Corequisites Prerequisite: coursework in ML, data science, law and policy or ethics, calculus and algebra, algorithms, and python programming. Recommended prerequisite: coursework in theory and optimization.

  • Students will be able to identify technical and operational models for energy delivery systems and appreciate why energy consumption in urban environments is bound to continue to be the most significant source of emissions under the status quo.
  • Students will learn about and analyze emerging technological solutions in wide area sensing and IoT networks, machine learning and decision models that support the coordination the distributed renewable resources on the supply side with the flexible demand of electricity in urban environments.
  • Students will identify security challenges that are unique of cyber-physical infrastructures and need to be addressed to advance to rip the benefits of digital technology in the field.
  • Through assignments and projects, the students will gain hands-on experience in demonstrating on how to apply novel data models, network technology and software tools that encompass the various topics covered in the class.

View Enrollment Information

Syllabi: none
  •   Regular Academic Session. 

  • 3 Credits Stdnt Opt

  • 19847 ECE 5235   LEC 030

  • Instruction Mode: In Person
    Taught in NYC. Enrollment limited to Cornell Tech Students only.