---
title: "从76,000篇能源系统研究中自动化提取技术经济数据"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.19178v1"
permalink: "https://feed.astralor.com/items/itm_e14b589eddf46cc0280be767"
published: "2026-07-21T15:14:32.000Z"
score: 71
channel: "tech"
tags: ["arXiv","FAIR数据库","交互式仪表盘","元分析","能源系统建模","自动数据提取"]
---

# 从76,000篇能源系统研究中自动化提取技术经济数据

## AI 点评

> 这是一篇将NLP技术应用于科学文献挖掘的典范之作，对能源建模和AI信息提取两个领域都有重要启发。对于从事能源系统研究、技术经济分析或科学元分析的读者尤其值得关注，它展示了如何将海量文献转化为结构化知识库。

## 速览

本论文提出一种高精度自动化提取方法，从2010年以来的76,000篇能源系统研究中提取了320万个结构化定量数据点和2000万个元数据条目，涵盖多种技术、方法论和系统特征。该FAIR数据库不仅为模型提供输入数据，还使能源系统文献本身可分析，揭示了学术假设与实证数据之间的偏差，以及研究优先级在技术、地区和时间上的差异。交互式仪表盘便于社区用户按需过滤、分析和下载数据。

## 关键点

- 从76,000篇能源系统研究中自动化提取定量数据
- 构建包含320万数据点和2000万元数据条的FAIR数据库
- 揭示学术假设与实证数据之间的偏差
- 提供交互式仪表盘便于社区使用

## 全文

Energy system models guide societally important decisions, but their credibility rests on quantitative assumptions that are difficult to source and audit. Meta-analyses can improve transparency and modeling practices, but the rapid growth of publications makes manual information extraction increasingly impractical. Consequently, databases are updated infrequently and efforts are often duplicated across research groups. Here, we demonstrate the highly accurate automated extraction of quantitative information from 76,000 energy system studies published since 2010. We compile 3.2 million structured quantitative data points together with 20 million associated metadata entries, spanning a broad spectrum of technologies, methodological approaches and system characteristics. Beyond providing input data for models, the resulting FAIR database make the energy systems literature itself analysable. We show where academic assumptions diverge from empirical observed data, and how research priorities vary at scale across technologies, regions and time. To facilitate broad use within the community, the database is provided through an interactive dashboard, enabling users to filter, analyse and download data according to their specific research needs.

---

引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_e14b589eddf46cc0280be767