Leveraging Large Language Models for Wind Energy Assessment
Abstract
Economic growth and development require a consistent supply of energy. Energy which has mainly been supplied from fossil fuels. The impacts of these on the environment such as global warming, raised an alarm on their use. As a result other sources of energy such as wind energy are used as alternatives for electricity production. Wind energy assessment nevertheless faces barriers due to its stochastic nature. This later creates various regimes, which traditional models can't always fit thereby producing poor estimates. In this work, we aim to use Large Language Models (LLMs) to predict the wind potential in a given location. Through this approach, we aim at lifting the barrier on energy problems in developing countries by providing knowledge on the state of wind energy in given locations.
Cite
Text
Zebaze et al. "Leveraging Large Language Models for Wind Energy Assessment." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I28.35235Markdown
[Zebaze et al. "Leveraging Large Language Models for Wind Energy Assessment." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/zebaze2025aaai-leveraging/) doi:10.1609/AAAI.V39I28.35235BibTeX
@inproceedings{zebaze2025aaai-leveraging,
title = {{Leveraging Large Language Models for Wind Energy Assessment}},
author = {Zebaze, Janice Anta and Jiomekong, Azanzi and Souopgui, Innocent and Kenmoe, Germaine Djuidje},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2025},
pages = {29315-29316},
doi = {10.1609/AAAI.V39I28.35235},
url = {https://mlanthology.org/aaai/2025/zebaze2025aaai-leveraging/}
}