  In this paper, we employ a 1D deep convolutional generative adversarial
network (DCGAN) for sequential anomaly detection in energy time series data.
Anomaly detection involves gradient descent to reconstruct energy
sub-sequences, identifying the noise vector that closely generates them through
the generator network. Soft-DTW is used as a differentiable alternative for the
reconstruction loss and is found to be superior to Euclidean distance.
Combining reconstruction loss and the latent space's prior probability
distribution serves as the anomaly score. Our novel method accelerates
detection by parallel computation of reconstruction of multiple points and
shows promise in identifying anomalous energy consumption in buildings, as
evidenced by performing experiments on hourly energy time series from 15
buildings.
