以数据为导向的冷却需求的表征,在一个共同位于商业建筑的投资组合
Aqsa Naeem1, Sally M Benson1, Jacques A de Chalendar1
1Department of Energy Science and Engineering, Stanford University, Stanford, CA, USA.
iScience
|August 2, 2024
概括
由于气候变化,商业建筑面临着越来越多的冷却需求. 数据驱动的模型揭示了不同建筑类型的冷却负载的显著差异,突出了能源效率的机会.
科学领域:
- 建筑的能源分析
- 在建筑环境中适应气候变化.
- 可持续建筑业务的运营.
背景情况:
- 商业建筑的冷却需求日益增加是一个越来越令人担忧的问题,气候变化加剧了这一问题.
- 建筑物有很长的寿命和缓慢的库存周转率,导致不断变化的能源消耗模式.
- 了解冷却负载变化对于能源效率和气候适应性至关重要.
研究的目的:
- 开发简单,可解释的数据驱动模型,用于分析不同建筑物的冷却负载.
- 识别和量化影响冷却消耗的因素,包括天气和占用率.
- 为诊断低效率和规划气候影响提供见解.
主要方法:
- 使用了五年建筑运营数据集.
- 开发了数据驱动的模型,包括天气和占用特征.
- 分析了不同建筑类型的冷却负载,控制了地理差异.
主要成果:
- 模型实现了对冷却负载的高预测精度 (R-平方为81%-87%).
- 在基负荷冷却 (0.504.4 MJ/m2/day) 中观察到显著的异质性,医疗保健设施最高,住所最低.
- 室外温度每升高1°C,冷却消耗量增加7.6%-9.8%,周末办公室降低27%.
结论:
- 数据驱动的模型有效地预测建筑冷却负载,并揭示显著的变化.
- 洞察力可以实现有针对性的能源效率改进和性能跟踪.
- 研究结果支持积极规划气候变化对建筑能源使用的影响.
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