使用机器学习的印度806个煤电厂的运行站热量数据集
Yifu Ding1, Jansen Wong2, Serena Patel1
1MIT Energy Initiative, Massachusetts Institute of Technology, United States.
Data in brief
|August 21, 2025
概括
这项研究使用机器学习创建了印度煤电厂效率的综合数据集. 这些发现支持印度
科学领域:
- 能源政策
- 环境科学
- 机器学习
背景情况:
- 印度的目标是到2070年实现零排放,
- 煤炭发电仍然占主导地位,在2025年占产能的47%,占发电的70%以上.
- 对于印度能源转型来说, 脱碳化现有煤电厂至关重要.
研究的目的:
- 解决印度煤电厂运营站热率 (SHR) 缺乏综合数据的问题.
- 为印度806个煤电厂单位开发最广泛的SHR数据集.
- 支持印度可再生能源目标的能源和环境政策制定.
主要方法:
- 利用现有数据库收集煤电厂的运行数据.
- 使用机器学习 (ML) 模型创建SHR数据集.
- 结合了水资源压力和煤炭价格等环境因素来提高预测准确度.
主要成果:
- 开发了一个涵盖806个印度煤电厂单位的综合数据集.
- 数据集为热效率变化提供了详细的见解.
- 综合环境因素提高了SHR预测的准确性.
结论:
- 这是迄今为止印度煤电厂最全面的SHR数据集.
- 这种资源有助于了解和提高煤电厂的效率.
- 促进印度能源和环境政策的明智决策.
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