综合能源需求和使用数据用于建筑自动化
Philipp Heer1, Curdin Derungs2, Benjamin Huber2
1Swiss Federal Laboratories for Materials Science and Technology, Urban Energy System Laboratories, UESL, Dübendorf, Switzerland. philipp.heer@empa.ch.
Scientific data
|May 8, 2024
概括
这项研究发布了NEST平台的四年数据集,详细介绍了建筑物的能源消耗和居住者的行为. 这些数据使得数据驱动的方法能够优化建筑功能和能源效率.
科学领域:
- 建筑科学 建筑科学
- 能源效率 能源效率 能源效率
- 数据科学数据科学数据科学
背景情况:
- 建筑物占全球能源消耗的近一半,这凸显了对舒适度和能源效率的研究的需要.
- 缺乏公开可用的数据阻碍了建筑功能的优化和数据驱动的建筑自动化的广泛采用.
- 数据驱动的方法对于使建筑自动化具有经济可行性和可访问性至关重要.
研究的目的:
- 解决公共可用数据的缺口,以优化建筑性能.
- 为研究界提供NEST平台的详细测量数据.
- 为不同建筑类型 (办公室和住宅) 提供数据驱动的学习.
主要方法:
- 收集并策划了NEST平台内的三座建筑的综合数据集.
- 记录了四年的数据,时间分辨率为1分钟.
- 包括有关能源消耗,建筑运行和居住者习惯的详细信息.
主要成果:
- 一个公开可用的数据集,包含能源消耗 (电力,供暖,冷却,家庭热水),建筑运行 (设置点,门开口,窗户) 和居住者的行为 (存在,百叶窗,厨房使用,淋浴).
- 数据集跨越了四年,具有高时间分辨率 (1分钟).
- 能够研究不同的建筑类型,包括办公室和住宅环境.
结论:
- 发布的数据集为推进建筑能效和自动化研究提供了宝贵的资源.
- 促进数据驱动的建模,以了解和优化建筑功能.
- 支持与建筑设计,运行和居住者的行为相互作用相关的研究差距.
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