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Predicting the indoor thermal data for heating season based on short-term measurements to calibrate the simulation set-points

机译：根据短期测量结果来预测供暖季节的室内热数据，以校准模拟设定点

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This study aims to predict the hourly thermal performance data during the heating season based on short-term measured data by comparing the results with the simulated data for a big scale residential building. The measurement period was done for about 31.5 days during the heating season. Alternatively, the heating season was evaluated with the simulated heating consumption data as the period from November 15th to March 31st (137 days) in order to predict the data for inside temperature and relative humidity. Relatedly, Artificial Neural Network (ANN) was designed for the prediction model. Outside dry-bulb temperature, outside dew-point temperature, wind speed, wind direction, atmospheric pressure, solar azimuth, and heating consumption were set as independent variables (inputs) along with temperature and humidity as targeted variables of the model. Four FeedForword - BackPropagation of ANNs were used as a network for each measurement point inside of the building where each ANN includes three layers, defined as input layers, hidden layer, and output layer. A thermal dataset for the heating season was composed by validating the result of the prediction with measured data, which saved about 77% of the heating season measurement's time. Then, by comparing composed dataset with the simulated thermal data, it was obtained that the heating system is performing well and close to the expectations. The approached prediction work provides the possibility to apply real-time calibration with the monitoring system which was already implemented in the building. Therefore, the produced dataset would ensure the quality of the real-time measured data for any unexpected condition during the building operation. as well as the accuracy of simulated data for any unforeseen inputs. Results show that the ANN model might be used effectively to provide useful predictions for the indoor thermal data not only to cover the missing measured data, but also to support the calibration process, the monitoring system and validating the simulation set points. (c) 2019 Elsevier B.V. All rights reserved.
机译：本研究旨在通过将短期测量数据与大型住宅建筑的模拟数据进行比较，从而根据短期测量数据预测供暖季节的每小时热性能数据。在供暖季节中，测量期约为31.5天。另外，可以使用模拟的热量消耗数据（从11月15日到3月31日，137天）评估供暖季节，以预测内部温度和相对湿度的数据。相关地，为预测模型设计了人工神经网络（ANN）。将外部干球温度，外部露点温度，风速，风向，大气压力，太阳方位角和热量消耗设置为自变量（输入），并将温度和湿度作为模型的目标变量。 ANN的四个FeedForword-BackPropagation被用作建筑物内部每个测量点的网络，其中每个ANN包括三层，分别定义为输入层，隐藏层和输出层。通过用测得的数据验证预测结果来构成供暖季节的热数据集，从而节省了约77％的供暖季节测量时间。然后，通过将组成的数据集与模拟的热数据进行比较，可以得出加热系统运行良好且接近预期的结果。即将进行的预测工作提供了将实时校准与已在建筑物中实施的监控系统一起应用的可能性。因此，生成的数据集将确保在建筑物运行过程中任何意外情况下实时测量数据的质量。以及任何意外输入的模拟数据的准确性。结果表明，人工神经网络模型可以有效地为室内热数据提供有用的预测，不仅可以覆盖丢失的测量数据，还可以支持校准过程，监控系统和验证模拟设定点。 （c）2019 Elsevier B.V.保留所有权利。

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