  Anomaly detection is a prominent data preprocessing step in learning
applications for correction and/or removal of faulty data. Automating this data
type with the use of autoencoders could increase the quality of the dataset by
isolating anomalies that were missed through manual or basic statistical
analysis. A Simple, Deep, and Supervised Deep Autoencoder were trained and
compared for anomaly detection over the ASHRAE building energy dataset. Given
the restricted parameters under which the models were trained, the Deep
Autoencoder perfoms the best, however, the Supervised Deep Autoencoder
outperforms the other models in total anomalies detected when considerations
for the test datasets are given.
