Task 4.1

Community-focused energy demand forecasting

Researchers are Dr. Zhang, Dr. Wang, Dr. Shi, Dr. Spangler, and Dr. Cui.

This task is dedicated to developing community-level load forecasting models. Utilizing historical energy usage data from utility billing systems and additional insights from the American Time Use Survey, we will create predictive models for community-level energy demand employing recurrent neural networks (RNNs).

Task 4.2

AI-powered spatial-temporal renewable forecasting.

Researchers are Dr. Shi, Dr. Junod, Dr. Zhang, Dr. Dadgostari, and Dr. Hu.

This task focuses on advancing the adaptivity and accuracy of renewable energy forecasting within the digital twin, a critical aspect for sustainable energy planning and operation. The task addresses the integration of long-term climate changes and weather extremes into the behavior modeling of energy prosumers.

Task 4.3

High-fidelity future scenario generation.

Researchers are Dr. Zhang, Dr. Cui, Dr. Shanahan, Dr. Shi, Dr. Spangler.

This task focuses on creating high-fidelity power system operating scenarios, particularly under extreme weather conditions, to enhance the digital twin’s predictive capabilities.

Task 4.4

Development of an integrated climate-energy-community digital twin platform.

Researchers are Dr. Cui, Dr. Zhang, Dr. Shi, Dr. Wang, Dr. Hu, Dr. Han, Dr. Sullivan, and Dr. Christy.

This task aims to create an AI-driven digital twin framework that transcends mere power systems replication to encompass the entire climate-energy-community nexus. By integrating physical power system models with state-of-the-art climate solution simulators like En-ROADS and the C-ROADS climate policy model, the framework will be capable of conducting comprehensive simulations.