Anomaly detection using convolutional autoencoder with residual gated recurrent unit and weak supervision for photovoltaic thermal heat pump system

Utilizing Convolutional Autoencoders with Residual Bi-Directional Gated Recurrent Unit bottleneck offers an advanced approach for detecting anomalies in heat pumps. Given the importance of heat pumps in achieving decarbonization goals for residential heating and cooling, accurate diagnosis of their health issues is essential for improving their efficiency and reducing environmental impact. Traditional diagnostic techniques, such as visual inspection, thermography, electrical testing, pressure measurements and temperature differentials require skilled technicians. These methods face some challenges due to the complexities of heat pump systems, which include components like compressors, coils, valves, and therefore require expertise in diagnosing their interactions. Our approach leverages Convolutional Autoencoders with Gated Recurrent Unit and Weak supervision to automatically detect anomalies and label significantly accelerating the diagnostic process. Our results show that thresholds based on the rolling mean outperform thresholds based on the actual errors by 6–10 %, depending on the random seeds. Additionally, the weak labels generated exhibit a positive correlation of at least 0.5 with error thresholds and 0.62 with rolling mean predictions.