A Deep Learning approach for Energy Disaggregation considering Embedded Devices
Resumo
Energy-saving becomes an increasingly important point since the demand for energy increases, and the resources for production are limited. One way to help consumers to save is by providing them with more transparency on how they are consuming. Energy disaggregation seeks to distinguish the electrical energy consumption of distinct devices connected to a single channel, in a non-intrusive way from a single measuring point. Deep learning is very promising in this field since they present better results when compared to previous models such as the Factorial Hidden Markov Model and Graph Signal Processing. In this work, we propose a deep learning approach for energy disaggregation focusing on its performance for embedded devices. Thus, we evaluate the UK-DALE database, and our proposal has an enhancement of 29.05% for the mean average error (MAE) and 8.66% for the signal aggregate error (SAE?) when comparing with previous work. Finally, we evaluate the scalability of our proposal for disaggregating multiple appliances considering an embedded device, and the results show that our proposal is well-suited for this application.
Palavras-chave:
Internet of Things, Mobile and Ubiquitous Computing Power, Energy and Thermal Aware Systems
Referências
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S. Makonin F. Popowich I. V. Bajić B. Gill L. Bartram "Exploiting hmm sparsity to perform online real-time nonintrusive load monitoring" IEEE Transactions on smart grid vol. 7 no. 6 pp. 2575-2585 2015.
T. Ji L. Liu T. Wang W. Lin M. Li Q. Wu "Non-intrusive load monitoring using additive factorial approximate maximum a posteriori based on iterative fuzzy c-means" IEEE Transactions on Smart Grid 2019.
B. Zhao L. Stankovic V. Stankovic "Blind non-intrusive appliance load monitoring using graph-based signal processing" 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP) pp. 68-72 2015.
V. Stankovic J. Liao L. Stankovic "A graph-based signal processing approach for low-rate energy disaggregation" 2014 IEEE symposium on computational intelligence for engineering solutions (CIES) pp. 81-87 2014.
K. He L. Stankovic J. Liao V. Stankovic "Non-intrusive load disaggregation using graph signal processing" IEEE Transactions on Smart Grid vol. 9 no. 3 pp. 1739-1747 2016.
F. M. Wittmann J. C. López M. J. Rider "Nonintrusive load monitoring algorithm using mixed-integer linear programming" IEEE Transactions on Consumer Electronics vol. 64 no. 2 pp. 180-187 2018.
R. Machlev Y. Levron Y. Beck "Modified cross-entropy method for classification of events in nilm systems" IEEE Transactions on Smart Grid 2018.
M. Z. A. Bhotto S. Makonin I. V. Bajić "Load disaggregation based on aided linear integer programming" IEEE Transactions on Circuits and Systems II: Express Briefs vol. 64 no. 7 pp. 792-796 2016.
J. Kelly W. Knottenbelt "The uk-dale dataset domestic appliance-level electricity demand and whole-house demand from five uk homes" Scientific data vol. 2 pp. 150007 2015.
C. Zhang M. Zhong Z. Wang N. Goddard C. Sutton "Sequence-to-point learning with neural networks for non-intrusive load monitoring" Thirty-Second AAAI Conference on Artificial Intelligence 2018.
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Y. Bengio et al. "Learning deep architectures for ai" Foundations and trends® in Machine Learning vol. 2 no. 1 pp. 1-127 2009.
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J. Kelly W. Knottenbelt "Neural nilm: Deep neural networks applied to energy disaggregation" Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments pp. 55-64 2015.
T. Sirojan B. Phung E. Ambikairajah "Deep neural network based energy disaggregation" 2018 IEEE International Conference on Smart Energy Grid Engineering (SEGE) pp. 73-77 2018.
D. Murray L. Stankovic V. Stankovic S. Lulic S. Sladojevic "Transferability of neural network approaches for low-rate energy disaggregation" ICASSP 2019-2019 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) pp. 8330-8334 2019.
K. S. Barsim B. Yang "On the feasibility of generic deep disaggregation for single-load extraction" 2018.
L. Mauch B. Yang "A new approach for supervised power disaggregation by using a deep recurrent lstm network" 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP) pp. 63-67 2015.
C. Shin S. Joo J. Yim H. Lee T. Moon W. Rhee "Subtask gated networks for non-intrusive load monitoring" 2018.
N. Batra J. Kelly O. Parson H. Dutta W. Knottenbelt A. Rogers A. Singh M. Srivastava "Nilmtk: an open source toolkit for non-intrusive load monitoring" Proceedings of the 5th international conference on Future energy systems pp. 265-276 2014.
D.-A. Clevert T. Unterthiner S. Hochreiter "Fast and accurate deep network learning by exponential linear units (elus)" 2015.
D. P. Kingma J. Ba "Adam: A method for stochastic optimization" 2014.
F. Chollet et al. "Keras" 2015 [online] Available: https://keras.io.
M. Abadi A. Agarwal P. Barham E. Brevdo Z. Chen C. Citro G. S. Corrado A. Davis J. Dean M. Devin S. Ghemawat I. Goodfellow A. Harp G. Irving M. Isard Y. Jia R. Jozefowicz L. Kaiser M. Kudlur J. Levenberg D. Mané R. Monga S. Moore D. Murray C. Olah M. Schuster J. Shlens B. Steiner I. Sutskever K. Talwar P. Tucker V. Vanhoucke V. Vasudevan F. Viégas O. Vinyals P. Warden M. Wattenberg M. Wicke Y. Yu X. Zheng "TensorFlow: Large-scale machine learning on heterogeneous systems" 2015 [online] Available: http://tensorflow.org/.
G. W. Hart "Nonintrusive appliance load monitoring" Proceedings of the IEEE vol. 80 no. 12 pp. 1870-1891 1992.
S. Pattem "Unsupervised disaggregation for non-intrusive load monitoring" 2012 11th International Conference on Machine Learning and Applications vol. 2 pp. 515-520 2012.
A. Viterbi "Error bounds for convolutional codes and an asymptotically optimum decoding algorithm" IEEE Transactions on Information Theory vol. 13 no. 2 pp. 260-269 April 1967.
O. Parson S. Ghosh M. Weal A. Rogers "Non-intrusive load monitoring using prior models of general appliance types" Twenty-Sixth AAAI Conference on Artificial Intelligence 2012.
S. Makonin F. Popowich I. V. Bajić B. Gill L. Bartram "Exploiting hmm sparsity to perform online real-time nonintrusive load monitoring" IEEE Transactions on smart grid vol. 7 no. 6 pp. 2575-2585 2015.
T. Ji L. Liu T. Wang W. Lin M. Li Q. Wu "Non-intrusive load monitoring using additive factorial approximate maximum a posteriori based on iterative fuzzy c-means" IEEE Transactions on Smart Grid 2019.
B. Zhao L. Stankovic V. Stankovic "Blind non-intrusive appliance load monitoring using graph-based signal processing" 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP) pp. 68-72 2015.
V. Stankovic J. Liao L. Stankovic "A graph-based signal processing approach for low-rate energy disaggregation" 2014 IEEE symposium on computational intelligence for engineering solutions (CIES) pp. 81-87 2014.
K. He L. Stankovic J. Liao V. Stankovic "Non-intrusive load disaggregation using graph signal processing" IEEE Transactions on Smart Grid vol. 9 no. 3 pp. 1739-1747 2016.
F. M. Wittmann J. C. López M. J. Rider "Nonintrusive load monitoring algorithm using mixed-integer linear programming" IEEE Transactions on Consumer Electronics vol. 64 no. 2 pp. 180-187 2018.
R. Machlev Y. Levron Y. Beck "Modified cross-entropy method for classification of events in nilm systems" IEEE Transactions on Smart Grid 2018.
M. Z. A. Bhotto S. Makonin I. V. Bajić "Load disaggregation based on aided linear integer programming" IEEE Transactions on Circuits and Systems II: Express Briefs vol. 64 no. 7 pp. 792-796 2016.
J. Kelly W. Knottenbelt "The uk-dale dataset domestic appliance-level electricity demand and whole-house demand from five uk homes" Scientific data vol. 2 pp. 150007 2015.
C. Zhang M. Zhong Z. Wang N. Goddard C. Sutton "Sequence-to-point learning with neural networks for non-intrusive load monitoring" Thirty-Second AAAI Conference on Artificial Intelligence 2018.
P. E. Utgoff D. J. Stracuzzi "Many-layered learning" Neural Computation vol. 14 no. 10 pp. 2497-2529 2002.
Y. Bengio et al. "Learning deep architectures for ai" Foundations and trends® in Machine Learning vol. 2 no. 1 pp. 1-127 2009.
I. Goodfellow Y. Bengio A. Courville Deep learning MIT press 2016.
J. Kelly W. Knottenbelt "Neural nilm: Deep neural networks applied to energy disaggregation" Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments pp. 55-64 2015.
T. Sirojan B. Phung E. Ambikairajah "Deep neural network based energy disaggregation" 2018 IEEE International Conference on Smart Energy Grid Engineering (SEGE) pp. 73-77 2018.
D. Murray L. Stankovic V. Stankovic S. Lulic S. Sladojevic "Transferability of neural network approaches for low-rate energy disaggregation" ICASSP 2019-2019 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) pp. 8330-8334 2019.
K. S. Barsim B. Yang "On the feasibility of generic deep disaggregation for single-load extraction" 2018.
L. Mauch B. Yang "A new approach for supervised power disaggregation by using a deep recurrent lstm network" 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP) pp. 63-67 2015.
C. Shin S. Joo J. Yim H. Lee T. Moon W. Rhee "Subtask gated networks for non-intrusive load monitoring" 2018.
N. Batra J. Kelly O. Parson H. Dutta W. Knottenbelt A. Rogers A. Singh M. Srivastava "Nilmtk: an open source toolkit for non-intrusive load monitoring" Proceedings of the 5th international conference on Future energy systems pp. 265-276 2014.
D.-A. Clevert T. Unterthiner S. Hochreiter "Fast and accurate deep network learning by exponential linear units (elus)" 2015.
D. P. Kingma J. Ba "Adam: A method for stochastic optimization" 2014.
F. Chollet et al. "Keras" 2015 [online] Available: https://keras.io.
M. Abadi A. Agarwal P. Barham E. Brevdo Z. Chen C. Citro G. S. Corrado A. Davis J. Dean M. Devin S. Ghemawat I. Goodfellow A. Harp G. Irving M. Isard Y. Jia R. Jozefowicz L. Kaiser M. Kudlur J. Levenberg D. Mané R. Monga S. Moore D. Murray C. Olah M. Schuster J. Shlens B. Steiner I. Sutskever K. Talwar P. Tucker V. Vanhoucke V. Vasudevan F. Viégas O. Vinyals P. Warden M. Wattenberg M. Wicke Y. Yu X. Zheng "TensorFlow: Large-scale machine learning on heterogeneous systems" 2015 [online] Available: http://tensorflow.org/.
Publicado
19/11/2019
Como Citar
SANTOS, Eduardo; FREITAS, Cristopher; AQUINO, Andre .
A Deep Learning approach for Energy Disaggregation considering Embedded Devices. In: SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SISTEMAS COMPUTACIONAIS (SBESC), 9. , 2019, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 73-80.
ISSN 2237-5430.
