Neural NILM: Deep Neural Networks Applied to Energy Disaggregation

By Jack Kelly, William John Knottenbelt. International Conference on Systems for Energy-Efficient Built Environments, 2015

898 citations recorded by the source corpus.

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.

Subfields: Building and Construction, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/7e629447855f235857cc2ae8d155a215f03fbf87

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Citations in the corpus

Tackling Climate Change with Machine Learning (2019, methodology): “[281] Jack Kelly and William Knottenbelt.”
Sequence-to-point learning with neural networks for nonintrusive load monitoring (2016, methodology): “Particularly, it has been applied to energy disaggregation [Kelly and Knottenbelt, 2015a] —both convolutional (CNN) and recurrent neural networks (RNN) were employed.”