This study introduces a framework for quality control of measured weather data, including anomaly detection, and infilling missing values. Weather data is a fundamental input to building performance simulations, in which anomalous values defect the results while missing data lead to an unexpected termination of the simulation process. Traditionally, infilling missing values in weather data is performed through periodic or linear interpolations. However, when missing values exceed many consecutive hours, the accuracy of traditional methods is subject to debate. This study demonstrates how Neural Networks can increase the accuracy of data imputation when compared to other supervised learning methods. The framework is validated by predicting missing temperature and relative humidity data for an observation site, through a network of nearby weather stations in Milan, Italy. Results show that the proposed method can facilitate real-time building simulations with accurate and rapid quality control.

翻译：本研究为测量天气数据的质量控制提供了一个框架,包括异常点检测和填充缺失值。天气数据是建筑性能模拟的基本投入,在模拟中,异常值将结果变差,而缺失的数据则导致模拟过程意外终止。传统上,通过定期或线性互换来填充天气数据中的缺失值。然而,当缺漏值连续超过许多小时时,传统方法的准确性将受到辩论。本研究显示神经网络与其他受监督的学习方法相比,如何提高数据估算的准确性。通过意大利米兰附近的气象站网络预测观测点的缺温和相对湿度数据,验证了这一框架。结果显示,拟议的方法可以促进实时模拟,并准确和快速地进行质量控制。