一个新的开源贝叶斯推理R平台的开发和绩效评估,用于建筑能源模型校准
Danlin Hou1, Dongxue Zhan1, Liangzhu Wang1
1Centre for Zero Energy Building Studies, Department of Building, Civil and Environmental Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montreal, QC H3G 1M8 Canada.
概括
本研究引入了一个自动化的贝叶斯推理平台,用于构建能源模型校准,增强不确定性量化和减少计算时间,以便更准确地预测能源消耗.
科学领域:
- 建筑科学 建筑科学
- 计算建模 计算建模
- 能源系统分析 能源系统分析
背景情况:
- 建筑能源消耗模型面临着固有的不确定性,需要强大的校准和灵敏度分析.
- 现有的确定性校准方法往往缺乏量化的不确定性,并且依赖于用户体验来进行参数选择.
- 需要严格的自动化方法来提高建筑能源模型的可靠性.
研究的目的:
- 开发一个自动化的贝叶斯推理校准平台,用于构建能源模型.
- 整合灵敏度分析和贝叶斯推理用于参数确定和不确定性量化.
- 使用马尔科夫链蒙特卡洛过程的元模型来提高计算效率.
主要方法:
- 开发了一个用于自动化贝叶斯推理校准的R包.
- 实施灵敏度分析以确定关键校准参数.
- 利用贝叶斯推理来量化模型参数中的不确定性.
- 采用了一个元模型来加速马尔科夫链蒙特卡洛 (MCMC) 模拟.
主要成果:
- 在炎热干旱的气候下,成功地在合成和真实高层建筑上展示了平台.
- 该平台有效地确定校准参数并量化相关的不确定性.
- 超级模型显著减少了校准过程的计算时间.
- 建立了校准参数,性能和元模型准确性之间的关系.
结论:
- 开发的贝叶斯推理平台在建筑能源模型校准的现有方法上提供了显著的优势.
- 该平台可在缩短的计算时间内提供可靠的建筑能效估计.
- 自动校准与不确定性量化提高了建筑能源模型的可靠性.
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