Classification of Anomaly Patterns in Integrated Energy Systems Based on Conditional Variational Autoencoder and Attention Mechanism

Abstract

By studying the classification of anomaly patterns in integrated energy systems, a deeper understanding of their operational status can be gained, leading to improved reliability and efficiency. This can ultimately result in reduced energy consumption and carbon emissions, contributing to sustainability efforts. This paper proposes a classification method that employs a conditional variational autoencoder and attention mechanism for deep clustering to identify anomaly patterns and distinguish between normal and anomaly datasets. The proposed model effectively addresses the issue of detecting anomalous periods in traditional anomaly detection methods. To account for the periodicity and coupling relationships of different loads, the model integrates sliding windows and attention mechanisms to improve the accuracy of detecting anomaly patterns. Firstly, during the encoder stage, a spatial attention mechanism is incorporated to extract features at each time step of the model input. Secondly, during the decoder stage, a temporal attention mechanism is introduced to perform feature extraction among the multiple time-step hidden layer states of the model input. The proposed method is applied to a typical integrated energy system and compared with existing methods. The experimental results demonstrate the effectiveness of the proposed method in accurately classifying the normal and anomaly patterns of integrated energy systems due to the internal clustering evaluation index.

Keywords

• deep learning
• deep clustering
• integrated energy systems
• time series
• multiple-load forecasting