关键词: 烘干系统, 时空特征提取, 能耗异常检测, 能耗异常指标

Abstract:

A method was proposed based on TCN-GAT and hybrid neural networks for identifying anomalies in energy usage for drying systems. First， a multi-scale temporal convolutional network （TCN） and a multi-head graph attention network （GAT） were introduced to capture the temporal and spatial properties of temperature， pressure， and other variables， respectively. An anomaly detection model was built upon a combination of back propagation neural network （BPNN） and variational autoencoder （VAE）. Furthermore， an energy consumption anomaly index was formulated based on prediction errors and reconstruction probability. The peak over threshold （POT） model was utilized to fit the Pareto distribution and establish an anomaly threshold. Finally， a case study was carried out at the painting workshop of a Chongqing automobile manufacturer， where Internet of Things （IoT） devices were used to gather real-world data. Data analysis was implemented to verify the effectiveness and superiority of the proposed method.

Key words: drying system, spatio-temporal feature extraction, energy consumption anomaly detection, energy consumption anomaly index