Energy Disaggregation

Energy disaggregation, also known as non-intrusive load monitoring (NILM), aims to identify individual appliance energy consumption from aggregated household electricity readings. Current research heavily utilizes deep learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks like LSTMs, often incorporating techniques like image processing (heatmaps) or transformer architectures to improve accuracy and efficiency. This field is crucial for enhancing energy efficiency through consumer awareness and improved demand-side management, while also presenting significant challenges in data privacy and the need for robust, transferable models applicable across diverse settings.