数据驱动的设计供暖负荷估算方法用于暖通空调设备选型
Data-driven estimation of design heating loads for HVAC equipment sizing
- Purdue University（普渡大学）
- Trane Technologies（特灵科技）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出两种数据驱动方法（基于智能恒温器数据和公用事业账单）估算设计供暖负荷，在74栋美国住宅上验证，发现现有设备和Manual J方法均显著高估负荷，有望改进设备选型。
AI中文摘要：
过大的供暖和制冷设备会不必要地增加前期成本、能源成本、污染物排放以及对电力基础设施的压力。本文开发了两种数据驱动的方法，用于在设计工况下估算供暖负荷，以改进设备选型。一种方法使用智能恒温器数据；另一种方法使用公用事业账单。我们在一个数据集上测试了这些方法，该数据集收集自美国五个气候区的74栋独栋单户住宅。该数据集包括智能恒温器时间序列数据、月度公用事业账单、天气数据、现有设备规格以及从从业者处购买的Manual J设计负荷计算（美国行业标准）。这两种方法各自具有较好的拟合优度统计量，并且彼此之间表现出良好的一致性。在74栋住宅的平均水平上，现有供暖设备比恒温器方法估算值大2.75倍（2.5至97.5经验百分位：1.34-6.52），比账单方法估算值大2.96倍（1.36-7.76）。按照从业者的实施方式，Manual J估算值平均比恒温器方法估算值大2.26倍（0.87-5.21），比账单方法估算值大2.35倍（1.02-5.28）。我们讨论了大规模实施这些数据驱动方法以及将数据驱动选型纳入行业标准的前景。
英文摘要：
Oversized heating and cooling equipment can unnecessarily increase up-front costs, energy costs, pollutant emissions, and strain on electrical infrastructure. This paper develops two data-driven methods for estimating heating loads at design conditions to improve equipment sizing. One method uses smart thermostat data; the other uses utility bills. We test the methods on a dataset that we gathered from 74 detached single-family houses in five United States climate zones. The dataset includes smart thermostat time-series data, monthly utility bills, weather data, existing equipment specifications, and Manual J design load calculations (the United States industry standard) purchased from practitioners. The two methods have strong goodness-of-fit statistics individually and show fair agreement with each other. On average over the 74 houses, existing heating equipment is 2.75 (2.5th to 97.5th empirical percentile: 1.34-6.52) times larger than the thermostat-method estimate and 2.96 (1.36-7.76 times larger than the bill-method estimate. As implemented by practitioners, the Manual J estimate is 2.26 (0.87-5.21) times larger on average than the thermostat-method estimate and 2.35 (1.02-5.28) times larger than the bill-method estimate. We discuss prospects for implementing the data-driven methods at scale and for incorporating data-driven sizing into industry standards.
