Supporting energy policy research with large language models: A case study in wind energy siting ordinances

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Grant Buster, Pavlo Pinchuk, Jacob Barrons, Ryan McKeever, Aaron Levine, Anthony Lopez
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引用次数: 0

Abstract

The recent growth in renewable energy development in the United States has been accompanied by a simultaneous surge in renewable energy siting ordinances. These zoning laws play a critical role in dictating the placement of wind and solar resources that are critical for achieving low-carbon energy futures. In this context, efficient access to and management of siting ordinance data becomes imperative. The National Renewable Energy Laboratory (NREL) recently introduced a public wind and solar siting database to fill this need. This paper presents a method for harnessing Large Language Models (LLMs) to automate the extraction of these siting ordinances from legal documents, enabling this database to maintain accurate up-to-date information in the rapidly changing energy policy landscape. A novel contribution of this research is the integration of a decision tree framework with LLMs. Our results show that this approach is 85 to 90 % accurate with outputs that can be used directly in downstream quantitative modeling. We discuss opportunities to use this work to support similar large-scale policy research in the energy sector. By unlocking new efficiencies in the extraction and analysis of legal documents using LLMs, this study enables a path forward for automated large-scale energy policy research.
用大型语言模型支持能源政策研究:风能选址条例案例研究
最近,美国可再生能源开发的增长伴随着可再生能源选址条例的同时激增。这些分区法在决定风能和太阳能资源的位置方面发挥着至关重要的作用,而风能和太阳能资源对于实现低碳能源的未来至关重要。在这种情况下,有效地获取和管理选址条例数据变得势在必行。美国国家可再生能源实验室(NREL)最近推出了一个公共风能和太阳能选址数据库,以满足这一需求。本文介绍了一种利用大型语言模型 (LLM) 从法律文件中自动提取这些选址条例的方法,从而使该数据库能够在瞬息万变的能源政策环境中保持准确的最新信息。这项研究的一个新贡献是将决策树框架与 LLMs 相结合。我们的研究结果表明,这种方法的准确率在 85% 到 90% 之间,其输出结果可直接用于下游定量建模。我们讨论了利用这项工作支持能源领域类似大规模政策研究的机会。通过利用 LLMs 提高法律文件提取和分析的效率,本研究为自动化大规模能源政策研究开辟了一条前进之路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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