The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
Open accessAbstract
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.
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Advances in Neural Information Processing Systems 25
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336
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D'Incecco, M., Squartini, S., & Zhong, M. (2020) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2938068 · Cited by 330
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Metadata
- Authors
- Kelly, J., & Knottenbelt, W.
- Venue
- Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments
- Publisher
- ACM
- Published
- 2015-11-04
- Pages
- 55-64
- OpenAlex
- W1479651931
- Cited by
- 891
- References
- 28
- FWCI
- 43.88
- Type
- conference paper
- Language
- en
- Open access
- green
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