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Bayesian estimation of a building's base temperature for the calculation of heating degree-days

机译：用于计算供暖天数的建筑物基础温度的贝叶斯估计

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摘要

Measuring the energy efficiency of a building requires adjusting for factors that affect the energy demand, such as the weather. When dealing with heating energy demand, this implies calculating heating degree-days for that building from climate data and the 'building's base temperature. This work describes a method for estimating a building's base temperature from periodic energy consumption records such as utility bills and weather data. This method derives not only the base temperature, but also the building's heat loss coefficient, base load, and daily heating variability. Unlike other techniques, this method uses Bayesian inference and provides exact confidence intervals. The theory is laid out before testing the method first on synthetic, "ideal" data, then on a set of real energy consumption data. The heating degree-days calculated from this estimated base temperature are almost perfectly proportional to the heating demand, unlike those obtained by the use of an officially recommended base temperature. The method is implemented in a freely available package in the R programming environment. (C) 2016 Elsevier B.V. All rights reserved.
机译：测量建筑物的能源效率需要调整影响能源需求的因素，例如天气。在处理供暖能源需求时，这意味着要根据气候数据和“建筑物的基准温度”来计算建筑物的供暖度日。这项工作描述了一种根据定期能耗记录（例如水电费和天气数据）估算建筑物基本温度的方法。这种方法不仅可以得出基础温度，还可以得出建筑物的热损失系数，基础负荷和每日的加热变化。与其他技术不同，此方法使用贝叶斯推断并提供准确的置信区间。在先对合成的“理想”数据进行测试，然后再对一组实际能耗数据进行测试之前，应先阐述该理论。根据估算的基本温度计算出的加热天数几乎与加热需求成正比，这与使用官方推荐的基本温度获得的天数不同。该方法在R编程环境中的免费软件包中实现。 （C）2016 Elsevier B.V.保留所有权利。

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