Deep learning has recently revolutionised a number of well-studied machine learning and signal processing problems, such as image recognition and handwriting recognition. Furthermore, long short-term memory architectures have demonstrated the effectiveness of applying recurrent neural networks to time series problems, such as speech synthesis. In addition to the impressive performance of these models, the elegance of learning features from data rather than hand crafting intuitive features is a highly compelling advantage over traditional methods.
In the past year, deep learning methods have also started to be applied to energy disaggregation. For example, Jack Kelly demonstrated at BuildSys 2015 how such models outperform common disaggregation benchmarks and are able to generalise to previously unseen homes. In addition, Lukas Mauch presented a paper at GlobalSIP 2015 describing how sub-metered data can be used to train networks to disaggregate single appliances from a building's total load. Most recently, Pedro Paulo Marques do Nascimento's master's thesis compared a variety of convolutional and recurrent neural networks across a number of appliances present in the REDD data set. Each piece of research demonstrates that there's real potential to apply deep learning to the problem of energy disaggregation.
However, two critical issues still remain. First, are the huge volumes of sub-metered data available which are required to train such models? Second, are the computational requirements of training these models practical? Fortunately, training can be performed offline if only general models of appliance types are to be learned. However, if learning is required for each individual household, surely this will need to take place on cloud infrastructure rather than embedded hardware. I hope we'll get closer to answering these questions at this year's international NILM conference in Vancouver!
