@inproceedings{simbuild2024_2265,
	doi = {},
	url = {https://publications.ibpsa.org/conference/paper/?id=simbuild2024_2265},
	year = {2024},
	month = {May},
	publisher = {IBPSA-USA},
	author = {Apoorv Khanuja and  Amanda L. Webb},
	title  = {Can LLMs Understand EEMs? Using Large Language Models To Manage Building Energy Efficiency Measure Data},
	booktitle = {Proceedings of SimBuild Conference 2024},
	volume  = {11},
	isbn = {},
	address  = {Denver, Colorado},
	series  = {IBPSA-USA Building Simulation Conference},
	pages = {407--416},
	abstract = {Large language models (LLMs) have the potential to significantly enhance building data exchange. They hold special promise for textual data like energy efficiency measures (EEMs), but have not yet been applied in this domain. To address this gap, a novel methodology was developed using LLMs to parse and compare two distinct EEM lists. EEM names in both lists were processed through an LLM, and, for each EEM in the first list, the most similar EEM in the second list was identified. The results showed considerable alignment between the model's top-predicted matches and the best match identified manually, demonstrating the value of LLMs for building data exchange. },
	issn = {},
	Organisation = {IBPSA-USA},
	Editors = {}
}