  Non-intrusive load monitoring (NILM) aims at energy consumption and appliance
state information retrieval from aggregated consumption measurements, with the
help of signal processing and machine learning algorithms. Representation
learning with deep neural networks is successfully applied to several related
disciplines. The main advantage of representation learning lies in replacing an
expert-driven, hand-crafted feature extraction with hierarchical learning from
many representations in raw data format. In this paper, we show how the NILM
processing-chain can be improved, reduced in complexity and alternatively
designed with recent deep learning algorithms. On the basis of an event-based
appliance recognition approach, we evaluate seven different classification
models: a classical machine learning approach that is based on a hand-crafted
feature extraction, three different deep neural network architectures for
automated feature extraction on raw waveform data, as well as three baseline
approaches for raw data processing. We evaluate all approaches on two
large-scale energy consumption datasets with more than 50,000 events of 44
appliances. We show that with the use of deep learning, we are able to reach
and surpass the performance of the state-of-the-art classical machine learning
approach for appliance recognition with an F-Score of 0.75 and 0.86 compared to
0.69 and 0.87 of the classical approach.
