Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
This work addresses energy disaggregation for smart grid applications, but it is incremental as it applies existing neural network methods to a known problem.
The paper tackled energy disaggregation by adapting three deep neural network architectures (LSTM, denoising autoencoders, and a regression network) to estimate appliance electricity consumption from whole-home data, achieving better F1 scores than existing methods like combinatorial optimization and factorial hidden Markov models, with good generalization to unseen houses.
Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand. Recently, deep neural networks have driven remarkable improvements in classification performance in neighbouring machine learning fields such as image classification and automatic speech recognition. In this paper, we adapt three deep neural network architectures to energy disaggregation: 1) a form of recurrent neural network called `long short-term memory' (LSTM); 2) denoising autoencoders; and 3) a network which regresses the start time, end time and average power demand of each appliance activation. We use seven metrics to test the performance of these algorithms on real aggregate power data from five appliances. Tests are performed against a house not seen during training and against houses seen during training. We find that all three neural nets achieve better F1 scores (averaged over all five appliances) than either combinatorial optimisation or factorial hidden Markov models and that our neural net algorithms generalise well to an unseen house.