Odysseas Krystalakos
ABSTRACT
Energy disaggregation is the process of extracting the power consumptions of multiple appliances from the total consumption signal of a building. Artificial Neural Networks (ANN) have been very popular for this task in the last decade. In this paper we propose two recurrent network architectures that use sliding window for real-time energy disaggregation. We compare this approach to existing techniques using six metrics and find that it scores better for multi-state devices. Finally, we compare ANNs that use Gated Recurrent Unit neurons against those using Long Short-Term Memory neurons and find that they perform equally.
- George W. Hart. 1985. Prototype nonintrusive appliance load monitor.Google Scholar
- George W. Hart. 1992. Nonintrusive appliance load monitoring. Proceedings of the IEEE. 80, 12, 1870--1891.Google ScholarCross Ref
- Alan Marchiori, Douglas Hakkarinen, Qi Han. and Lieko Earle. 2011. Circuit-Level Load Monitoring for Household Energy Management. IEEE Pervasive Computing. 10, 1, 40--48. Google ScholarDigital Library
- Dominik Egarter, Venkata P. Bhuvana and Wilfried Elmenreich 2015. PALDi: Online Load Disaggregation via Particle Filtering. IEEE Transactions on Instrumentation and Measurement. 64, 2, 467--477.Google ScholarCross Ref
- Oliver Parson, Siddhartha Ghosh, Mark Weal and Alex Rogers. 2014. An unsupervised training method for non-intrusive appliance load monitoring. Artificial Intelligence. 217, 1--19. Google ScholarDigital Library
- Francesca Paradiso, Federica Paganelli, Dino Giuli and Samuele Capobianco. 2016. Context-Based Energy Disaggregation in Smart Homes. Future Internet. 8, 1, 4.Google ScholarCross Ref
- Francesca Paradiso, Federica Paganelli, Antonio Luchetta, Dino Giuli and Pino Castrogiovanni. 2013. ANN-based appliance recognition from low-frequency energy monitoring data. 2013 IEEE 14th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM).Google ScholarCross Ref
- Jack Kelly and William Knottenbelt. 2015. Neural NILM. Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments - BuildSys '15. Google ScholarDigital Library
- Henning Lange. and Mario Berges. 2016. The Neural Energy Decoder: Energy Disaggregation by Combining Binary Subcomponents. Proceedings of the 3rd International Workshop on Non-Intrusive Load Monitoring.Google Scholar
- Lukas Mauch and Bin Yang. 2015. A new approach for supervised power disaggregation by using a deep recurrent LSTM network. 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP).Google ScholarCross Ref
- Chaoyun Zhang, Mingjun Zhong, Zongzuo Wang, Nigel Goddard and Charles Sutton. 2017. Sequence-to-point learning with neural networks for nonintrusive load monitoring. ArXiv e-prints. https://arxiv.org/abs/1612.09106.Google Scholar
- Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25, 1097--1105. Google ScholarDigital Library
- Alex Graves. 2013. Generating Sequences With Recurrent Neural Networks. ArXiv e-prints. https://arxiv.org/abs/1308.0850.Google Scholar
- Jack Kelly and William Knottenbelt. 2015. The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Scientific Data. 2, 150007.Google ScholarCross Ref
- Thi-Thu-Huong Le, Jihyun Kim and Howon Kim. 2016. Classification performance using gated recurrent unit recurrent neural network on energy disaggregation. 2016 International Conference on Machine Learning and Cybernetics (ICMLC).Google ScholarCross Ref
- Nipun Batra, Jack Kelly, Oliver Parson, Haimonti Dutta, William Knottenbelt, Alex Rogers, Amarjeet Singh and Mani Srivastava.2014. NILMTK. Proceedings of the 5th international conference on Future energy systems - e-Energy '14.Google Scholar
- Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. ArXiv e-prints. https://arxiv.org/abs/1612.09106.Google Scholar
- Junyoung Chung, Caglar Gulcehre, KyungHyun Cho and Yoshua Bengio. 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. ArXiv e-prints. https://arxiv.org/abs/1412.3555.Google Scholar
- Sliding Window Approach for Online Energy Disaggregation Using Artificial Neural Networks
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