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.