  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.
