The estimation of wall thermal properties by \emph{in situ} measurement enables to increase the reliability of the model predictions for building energy efficiency. Nevertheless, retrieving the unknown parameters has an important computational cost. Indeed, several computations of the heat transfer problem are required to identify these thermal properties. To handle this drawback, an innovative approach is investigated. The first step is to search the optimal experiment design among the sequence of observation of several months. A reduced sequence of observations of three days is identified which guarantees to estimate the parameter with the maximum accuracy. Moreover, the inverse problem is only solved for this short sequence. To decrease further the computational efforts, a reduced order model based on the modal identification method is employed. This \emph{a posteriori} model reduction method approximates the solution with a lower degree of freedom. The whole methodology is illustrated to estimate the thermal diffusivity of an historical building that has been monitored with temperature sensors for several months. The computational efforts is cut by five. The estimated parameter improves the reliability of the predictions of the wall thermal efficiency.
翻译:通过 \ emph{ 原地 度量对墙热特性进行估计,可以提高建筑能源效率模型预测的可靠性。 然而,检索未知参数具有重要的计算成本。 事实上,需要用数项计算热传导问题来确定这些热特性。 要处理这一退步,就要研究一种创新的方法。第一步是在几个月的观测序列中搜索最佳实验设计。确定3天的缩减观测序列,保证以最大精确度估计参数。此外,只有这一短顺序才能解决反向问题。为了进一步减少计算努力,采用了基于模式识别方法的减序模型。这一计算模型的减序方法以较低的自由度接近解决方案。整个方法用来估计用温度传感器监测了数月的历史建筑的热偏差性。计算工作减少了5天。估计参数提高了墙热效率预测的可靠性。