Non Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) aims to disaggregate total household energy consumption into individual appliance usage patterns using only the aggregate power signal, reducing the need for individual appliance sensors. Current research heavily utilizes deep learning models, particularly convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, often incorporating attention mechanisms to improve accuracy and efficiency, with a growing focus on addressing challenges like data sparsity, low sampling rates, and privacy concerns. NILM's significance lies in its potential for enhancing energy efficiency, enabling advanced demand-side management strategies, and providing valuable insights into energy consumption behaviors for both individual users and utility companies.