Attention boosted autoencoder for building energy anomaly detection

Significant energy savings can be realised from buildings if deviations from the usual operating conditions are detected early, and appropriate measures are taken. Building anomaly detection techniques automate identifying such instances by leveraging the high dimensional data collected from the installed smart meters. Autoencoders allow for dimensionality reduction and also model the underlying data distribution. However, these models treat features as independent quantities. In contrast, the current work investigates an attention mechanism with an autoencoder to include the correlations among the features. The value addition from the attention mechanism is demonstrated by comparing the model's reconstruction ability with an ANN-based autoencoder on synthetic datasets. The study identifies that adding an attention layer enables the encoder- decoder architecture to be robust to outliers in training data, thereby reducing the preprocessing required. Further, the model is tested on a real-world dataset, and the attention maps generated from the model are used to interpret the correlations among the features and across the time dimension, thereby establishing a human-interpretable way to understand the reconstruction from the model.