GridAI: Cloud-based Machine/Deep Learning for Power Grid Data Analytics
This project is an extension of the “May22-35: GridAI: Cloud-based Machine/Deep Learning for Power
Grid Data
Analytics” (http://sdmay22-35.sd.ece.iastate.edu/). This project aims to design and implement cloud-based
machine
learning or deep learning algorithms for grid data analytics. We use Google Cloud Platform (GCP) resources
for the
design and implementation. Students will receive all the required materials for working on the GCP. Students
will
receive all the required materials, including software resources of May22-35, for working on these
platforms.
Programming experience and a Linux working environment are two essential prerequisites. At the end of the
project,
you will learn cloud computing architecture, Machine Learning (ML), or Deep Learning (DL), and attain
hands-on
experience with the GCP resources. This software-based project includes the following modules to be
developed: 1)
Extend the existing design and implementation of the current GridAI web-based visualizations, including
advancing
the geographical Maps for the power grid data, and data analytics, 2) Advancing ML/DL-based voice assistant
for the
GridAI software for efficient search of power grid components and data, and 3) Test and validate the
application with
available power grid simulators such as OPAL-RT.