STNILM: Switch Transformer based Non-Intrusive Load Monitoring for short and long duration appliances

Abstract

Non-Intrusive Load Monitoring (NILM) is a technique used by contemporary energy management systems to predict and optimize appliance load distribution in real time. The real-time reduction of energy consumption and improvement of electricity efficiency are two major benefits of energy disaggregation. Transformer models have made NILM far better at forecasting device power values. Due to the absence of inductive bias in the local context, transformers may not be able to capture local signal patterns in sequence-to-point settings. In this work, we present a Switch Transformer based Non-Intrusive Load Monitoring (STNILM). STNILM utilizes switching and routing layers by replacing the vanilla transformer final layers to accurately estimate the power signals of short and long duration domestic appliances. It also uses self attention mechanisms to extract global dependencies between the aggregate and the domestic appliance signals. STNILM works with minimal dataset pre-processing and unbalanced. With extensive experiments and quantitative analysis, we demonstrate the efficiency and effectiveness of the proposed STNILM with considerable improvements in terms of accuracy and F1-score compared to state-of-the-art baselines.