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Estimation of temperature setpoints and heat transfer coefficients among residential buildings in Denmark based on smart meter data

机译：基于智能电表数据估算丹麦住宅建筑中的温度设定点和传热系数

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摘要

Thermal comfort preferences of occupants and their interactions with building systems are top influential factors of residential space heating demand. Consequently, housing stock models are sensitive to assumptions made on heating temperatures. This study proposes a heat balance approach, inspired by the classical degree-day method, applied to an extensive urban dataset. The goal of this analysis is to determine heterogeneous characteristics, such as temperature setpoints of heating systems and thermal envelope characteristics from an overall population of residential buildings. Measured energy data are utilized for the purpose of the study from the city of Aarhus, Denmark, where the energy usage for heating of circa 14,000 households was monitored over time via smart meters. These data are combined with actual weather data as well as data extracted by a national building database. Using linear regression and heat balance models, temperature setpoints for the whole dataset are determined with a median and average of 19 degrees C and 19.1 degrees C, respectively. Furthermore, building related characteristics such as thermal and ventilation losses per building and overall heat transfer coefficients are extracted at urban scale. The reliability of the method over its complexity is discussed with regards to the big sample that has been applied to. In general, the overall performance of the approach is satisfactory achieving a coefficient of determination with an average of 0.8, and is found to be in line with previous findings, considering also the high uncertainty associated with building-related input parameters. The extracted setpoint distribution should be transferrable across Scandinavia.
机译：居住者的热舒适偏好及其与建筑系统的相互作用是住宅空间供热需求的主要影响因素。因此，房屋存量模型对加热温度的假设很敏感。这项研究提出了一种受经典度日法启发的热平衡方法，该方法适用于广泛的城市数据集。该分析的目的是从住宅建筑的总体中确定异类特征，例如加热系统的温度设定点和热包络特征。丹麦奥胡斯市利用测得的能源数据进行研究，该地区通过智能电表随时间监测大约14,000户家庭的供暖能源使用情况。这些数据与实际天气数据以及国家建筑数据库提取的数据结合在一起。使用线性回归和热平衡模型，整个数据集的温度设定点的中位数和平均值分别为19摄氏度和19.1摄氏度。此外，在城市范围内提取了与建筑物相关的特性，例如每栋建筑物的热和通风损失以及总传热系数。关于已应用的大样本，讨论了该方法在复杂性方面的可靠性。通常，该方法的总体性能令人满意，可以实现平均系数为0.8的确定系数，并且考虑到与建筑物相关的输入参数相关的高度不确定性，因此与以前的发现相符。所提取的设定点分布应可在整个斯堪的纳维亚地区转移。

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