What is it about?
The project aims to generate synthetic household electricity data by simulating human interaction behaviors using a Large Language Model (LLM). By leveraging the LLM's ability to mimic these behaviors, we simulate agents that interact with and perform actions in controlled environments, creating synthetic energy data that is free from sensitive information and shareable.
Featured Image
Photo by Krissia Cruz on Unsplash
Why is it important?
We can more effectively generate and allocate electricity with a better understanding of how much electricity people use. This is one of the first papers to explore the use of large language models to simulate energy data. We simulated human behavior and generated synthetic electricity consumption data using a large language model. Simulating complex human behavior and corresponding electricity data is challenging. However, a large language model makes it possible with its vast world knowledge and capability for logical thinking.
Perspectives
We published this paper as part of the CSIRO/Pawsey Supercomputing Centre Student Vacation Program. This paper brings a unique perspective on how different areas of research can be combined, particularly large language models and energy.
Yusuke Miyashita
Read the Original
This page is a summary of: Can Private LLM Agents Synthesize Household Energy Consumption Data?, May 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3632775.3661993.
You can read the full text:
Resources
Contributors
The following have contributed to this page
