Adversarial Energy Disaggregation for Non-intrusive Load Monitoring

ZHEKAI DU, School of Computer Science and Engineering, University of Electronic Science and Technology of China, China JINGJING LI∗, School of Computer Science and Engineering, University of Electronic Science and Technology of China, China LEI ZHU, School of Information Science and Engineering, Shandong Normal University, China KE LU, School of Computer Science and Engineering, University of Electronic Science and Technology of China, China HENG TAO SHEN, School of Computer Science and Engineering, University of Electronic Science and Technology of China, China

[1]  Charles A. Sutton,et al.  Sequence-to-point learning with neural networks for nonintrusive load monitoring , 2016, AAAI.

[2]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[3]  G. W. Hart,et al.  Nonintrusive appliance load monitoring , 1992, Proc. IEEE.

[4]  Andrew Y. Ng,et al.  Energy Disaggregation via Discriminative Sparse Coding , 2010, NIPS.

[5]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[6]  Lucio Soibelman,et al.  User-Centered Nonintrusive Electricity Load Monitoring for Residential Buildings , 2011 .

[7]  Gissella Bejarano,et al.  Deep Latent Generative Models for Energy Disaggregation , 2019, AAAI.

[8]  Haimonti Dutta,et al.  NILMTK: an open source toolkit for non-intrusive load monitoring , 2014, e-Energy.

[9]  Paulo C. M. Meira,et al.  Towards reproducible state-of-the-art energy disaggregation , 2019, BuildSys@SenSys.

[10]  J. Zico Kolter,et al.  REDD : A Public Data Set for Energy Disaggregation Research , 2011 .

[11]  Howon Kim,et al.  Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature , 2017, Comput. Intell. Neurosci..

[12]  Ke Liu,et al.  Sequence-To-Subsequence Learning With Conditional Gan For Power Disaggregation , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[13]  Anastasios Doulamis,et al.  Context Aware Energy Disaggregation Using Adaptive Bidirectional LSTM Models , 2020, IEEE Transactions on Smart Grid.

[14]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[15]  Scott Dick,et al.  Toward Non-Intrusive Load Monitoring via Multi-Label Classification , 2017, IEEE Transactions on Smart Grid.

[16]  Bin Yang,et al.  A new approach for supervised power disaggregation by using a deep recurrent LSTM network , 2015, 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[17]  Jun Hu,et al.  Convolutional Sequence to Sequence Non-intrusive Load Monitoring , 2018, The Journal of Engineering.

[18]  Shubi Kaijage,et al.  A Survey on Non-Intrusive Load Monitoring Methodies and Techniques for Energy Disaggregation Problem , 2017, ArXiv.

[19]  Lucio Soibelman,et al.  A time-frequency approach for event detection in non-intrusive load monitoring , 2011, Defense + Commercial Sensing.

[20]  Martin Wagner,et al.  Enhancing neural non-intrusive load monitoring with generative adversarial networks , 2018 .

[21]  C. Rosenzweig,et al.  Attributing physical and biological impacts to anthropogenic climate change , 2008, Nature.

[22]  Shuai Han,et al.  Multi-Pattern Data Mining and Recognition of Primary Electric Appliances from Single Non-Intrusive Load Monitoring Data , 2019, Energies.

[23]  Chong-Yung Chi,et al.  Nonnegative Least-Correlated Component Analysis for Separation of Dependent Sources by Volume Maximization , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Wilsun Xu,et al.  SDP Relaxation with Randomized Rounding for Energy Disaggregation , 2016, NIPS.

[25]  Anastasios Doulamis,et al.  EnerGAN: A GENERATIVE ADVERSARIAL NETWORK FOR ENERGY DISAGGREGATION , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[26]  Michael I. Jordan,et al.  Factorial Hidden Markov Models , 1995, Machine Learning.

[27]  Corinna Fischer Feedback on household electricity consumption: a tool for saving energy? , 2008 .

[28]  Tommi S. Jaakkola,et al.  Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation , 2012, AISTATS.

[29]  Wonjong Rhee,et al.  Subtask Gated Networks for Non-Intrusive Load Monitoring , 2018, AAAI.

[30]  R. N. Elliott,et al.  American Council for an Energy-Efficient Economy , 2002 .

[31]  Jack Kelly,et al.  The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes , 2014, Scientific Data.

[32]  Kamin Whitehouse,et al.  Matrix Factorisation for Scalable Energy Breakdown , 2017, AAAI.

[33]  Lu Ke,et al.  Maximum Density Divergence for Domain Adaptation , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Charles A. Sutton,et al.  Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation , 2014, NIPS.

[35]  A. Longjun Wang,et al.  Non-intrusive load monitoring algorithm based on features of V–I trajectory , 2018 .

[36]  Ke Lu,et al.  Heterogeneous Domain Adaptation Through Progressive Alignment , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[37]  Jack Kelly,et al.  Neural NILM: Deep Neural Networks Applied to Energy Disaggregation , 2015, BuildSys@SenSys.

[38]  Terrence J. Sejnowski,et al.  ICA Mixture Models for Unsupervised Classification of Non-Gaussian Classes and Automatic Context Switching in Blind Signal Separation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[39]  Zhu Lei,et al.  Locality Preserving Joint Transfer for Domain Adaptation , 2019, IEEE Transactions on Image Processing.