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.

本文中，我们使用了一种1D深度卷积生成对抗网络（DCGAN）来进行能源时间序列数据中的顺序异常检测。通过梯度下降来重构能量子序列，通过生成器网络识别生成它们的噪声向量。我们使用可微的Soft-DTW作为重构损失的替代方法，发现其优于欧几里德距离。将重构损失和潜在空间的先验概率分布相结合可作为异常得分。实验证明，我们的新方法通过并行计算多个点的重构加速检测，并在15栋建筑的小时能源时间序列上显示出识别异常能耗的潜力。

具有软动态时间对齐和并行重构的生成对抗网络在能源时间序列异常检测中的应用