Shared Encoder with Attention for Non-Intrusive Appliance Load Monitoring

Non-intrusive load monitoring (NILM) is a methodology used to deduce the power consumption and operational status of electrical equipment by analyzing the aggregated power signal of a residential or commercial structure. Utilizing detailed electric load profiles obtained from smart meters has generated growing interest in employing this method for demand-side energy management within smart grids. NILM is a time series regression problem that involves appliance state classification, pattern recognition, and anomaly detection. Deep learning-based NILM techniques have shown promising results, but they are often applicable to a limited number of appliances due to their complex model architecture and huge computational cost. This paper proposes an improved method by using a shared convolutional encoder architecture for a multitask decoder network, adding multi-head attention blocks in between. The proposed approach outperforms state-of-the-art techniques in the publicly available REDD data set while requiring a reduced amount of training time.