A Multi-Task Deep Learning Approach for Non-Intrusive Load Monitoring of Multiple Appliances

This letter proposes a novel deep learning-based multi-task approach for non-intrusive monitoring of home appliances-the first of its kind-where a network can simultaneously estimate the states and disaggregate energies of multiple appliances. An attention-powered encoder-decoder network, comprising a convolutional layer and a long short-term memory, is deployed for the above tasks. Test results from two real-world datasets demonstrate the approach's feasibility, showcasing superior performance and reduced memory requirements.