Advanced proactive anomaly detection in multi-pattern home appliances for energy optimization

As far as home appliances are concerned, a major issue arises as malfunctions frequently go unnoticed by homeowners, consuming a significant amount of energy. Identifying this problem highlights the need for an error detection technique, which is vital to minimize energy waste while enhancing household appliance efficiency. This paper introduces an innovative method for advanced proactive anomaly detection in multi-pattern home appliances using variational autoencoders (VAE). Specifically, a CNN-LSTM based VAE integrating a novel dynamic threshold method is exploited for identifying abnormal consumption usage. The overall suggested framework is a complete integrated approach comprising a training and testing phase. Both phases are initiated from robust feature engineering to enhance the multipattern operation program classification. Additionally, a smoothing and decomposition process is applied to optimize the performance of the CNN-LSTM VAE. The efficacy of the method is evaluated on data from 26 different front-load washing machines, demonstrating its effectiveness in identifying anomaly points across various washing programs. Furthermore, compared to the same approach with a static threshold, the proposed method showed improvements of up to 11. 4% on the F1 score. Finally, simulated use case scenarios indicate a reduction of nearly 30% of energy consumption, due to error prevention. As a result, the suggested approach is a robust and applicable tool for energy and demand side management.