Non-Intrusive Load Monitoring with Fully Convolutional Networks
This work addresses the problem of efficient non-intrusive load monitoring for home energy management, though it is incremental as it builds on existing deep learning methods.
The paper tackles energy disaggregation by proposing a deep neural network architecture that achieves state-of-the-art performance with significantly improved computational efficiency, reducing training time by a factor of 32 and prediction time by a factor of 43.
Non-intrusive load monitoring or energy disaggregation involves estimating the power consumption of individual appliances from measurements of the total power consumption of a home. Deep neural networks have been shown to be effective for energy disaggregation. In this work, we present a deep neural network architecture which achieves state of the art disaggregation performance with substantially improved computational efficiency, reducing model training time by a factor of 32 and prediction time by a factor of 43. This improvement in efficiency could be especially useful for applications where disaggregation must be performed in home on lower power devices, or for research experiments which involve training a large number of models.