用于建筑性能监测和分析的1-24小时干球温度间隙的恢复-第1部分

Junjun Hu, Oluwaseyi T. Ogunsola, Li Song, R. McPherson, Meijun Zhu, Y. Hong, Sheng Chen
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引用次数: 7

摘要

建筑能源系统改造和改造调试项目为节约能源提供了巨大的机会。建筑物的能源消耗,特别是暖通空调系统,受到天气条件的显著影响。然而,由于数据传输问题、数据质量保证方法、传感器故障或许多其他原因,短期或长期气候数据经常丢失。这些气候数据上的差距继续给暖通空调工程师在监测和验证建筑能源性能方面带来挑战。本文研究了使用线性插值、拉格朗日插值和三次样条插值技术的八种经典方法,以及使用两种新开发的方法(即基于角度的插值和基于corr的插值)的十一种方法,以恢复时间序列中缺失的24小时干球温度数据,用于建筑性能监测和分析。使用11个一年每小时的数据集来评估这19种不同方法的性能。每种方法都应用于处理随机产生的人工间隙。根据估计值与实测值之间的差异,进行两种类型的比较。首先用MAE、RMSE和STDBIAS三个评价指标进行比较。第二次比较是基于在特定误差阈值范围内可以通过方法估计的总数据的百分比,包括1°F(0.56°C), 2°F(1.11°C), 3°F(1.67°C)和5°F(2.78°C),从测量值。对比结果表明,线性插值在填充1 ~ 2 h间隙时效果最好,拉格朗日插值(Lag2L2R)在填充3 ~ 8 h间隙时效果优于其他方法,而基于corr的插值方法(Corr1L1R24Avg)在填充9 ~ 24 h间隙时效果更好。本文通过ASHRAE 1413研究项目介绍了第一部分的研究成果。第二部分的结果侧重于填补长期干球温度差距的方法。
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Restoration of 1–24 hour dry-bulb temperature gaps for use in building performance monitoring and analysis—Part I
Building energy system retrofit and retro-commissioning projects present tremendous opportunities to save energy. Energy consumption in buildings, especially HVAC systems, is significantly impacted by weather conditions. However, short- or long-term climatic data are frequently missing because of data transmission problems, data quality assurance methods, sensor malfunction, or a host of other reasons. These gaps in climatic data continue to provide challenges for HVAC engineers in monitoring and verifying building energy performance. This article examines eight classical approaches that use Linear interpolation, Lagrange interpolation, and Cubic Spline interpolation techniques, and eleven approaches that use two newly developed methods, i.e., Angle-based interpolation and Corr-based interpolation, to restore up to 24 h of missing dry-bulb temperature data in a time series for use in building performance monitoring and analysis. Eleven one-year hourly data sets are used to evaluate the performance of these 19 different methods. Each method is applied to deal with artificial gaps that are generated randomly. In terms of the difference between estimated values and measured values, two types of comparisons are carried out. The first comparison is conducted with three evaluation indices: MAE, RMSE, and STDBIAS. The second comparison is based on the percentage of the total data that can be estimated by an approach within specific error thresholds, including 1°F (0.56°C), 2°F (1.11°C), 3°F (1.67°C), and 5°F (2.78°C), from measured values. The comparison results show that Linear interpolation performs best when filling 1–2 h gaps, Lagrange interpolation (Lag2L2R) outperforms other methods when gaps are 3–8 h long, and the Corr-based interpolation method (Corr1L1R24Avg) is a better technique for filling 9–24 h gaps. This article presents the first part of the research results through the ASHRAE 1413 research project. The second part of the results focuses on methods to filling long-term dry-bulb temperature gaps.
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HVAC&R Research
HVAC&R Research 工程技术-工程:机械
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