A Hybrid Deep Learning Framework for Non-Intrusive Load Monitoring

Abstract

In recent years, load disaggregation and non-intrusive load-monitoring (NILM) methods have garnered widespread attention for optimizing energy management systems, becoming crucial tools for achieving energy efficiency and analyzing power consumption. However, existing NILM methods face challenges in accurately handling appliances with multiple operational states and suffer from low accuracy and poor computational efficiency, particularly in modeling long-term dependencies and complex appliance load patterns. This article proposes an improved NILM model optimized based on transformers. The model first utilizes a convolutional neural network (CNN) to extract features from the input sequence and employs a bidirectional long short-term memory (BiLSTM) network to model long-term dependencies. Subsequently, multiple transformer blocks are used to capture dependencies within the sequence. To validate the effectiveness of the proposed model, we applied it to real-world household energy datasets: UK-DALE and REDD. Compared with suboptimal models, our model significantly improves the F1 score by 24.5% and 22.8%.