  As the number of installed meters in buildings increases, there is a growing
number of data time-series that could be used to develop data-driven models to
support and optimize building operation. However, building data sets are often
characterized by errors and missing values, which are considered, by the recent
research, among the main limiting factors on the performance of the proposed
models. Motivated by the need to address the problem of missing data in
building operation, this work presents a data-driven approach to fill these
gaps. In this study, three different autoencoder neural networks are trained to
reconstruct missing short-term indoor environment data time-series in a data
set collected in an office building in Aachen, Germany. This consisted of a
four year-long monitoring campaign in and between the years 2014 and 2017, of
84 different rooms. The models are applicable for different time-series
obtained from room automation, such as indoor air temperature, relative
humidity and $CO_{2}$ data streams. The results prove that the proposed methods
outperform classic numerical approaches and they result in reconstructing the
corresponding variables with average RMSEs of 0.42 {\deg}C, 1.30 % and 78.41
ppm, respectively.
