Implementation Strategy of Convolution Neural Networks on Field Programmable Gate Arrays for Appliance Classification Using the Voltage and Current (V-I) Trajectory

Specific information about types of appliances and their use in a specific time window could help determining in details the electrical energy consumption information. However, conventional main power meters fail to provide any specific information. One of the best ways to solve these problems is through non-intrusive load monitoring, which is cheaper and easier to implement than other methods. However, developing a classifier for deducing what kind of appliances are used at home is a difficult assignment, because the system should identify the appliance as fast as possible with a higher degree of certainty. To achieve all these requirements, a convolution neural network implemented on hardware was used to identify the appliance through the voltage and current (V-I) trajectory. For the implementation on hardware, a field programmable gate array (FPGA) was used to exploit processing parallelism in order to achieve optimal performance. To validate the design, a publicly available Plug Load Appliance Identification Dataset (PLAID), constituted by 11 different appliances, has been used. The overall average F-score achieved using this classifier is 78.16% for the PLAID 1 dataset. The convolution neural network implemented on hardware has a processing time of approximately 5.7 ms and a power consumption of 1.868 W.

[1]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[2]  Thomas G. Habetler,et al.  Electric Load Classification by Binary Voltage–Current Trajectory Mapping , 2016, IEEE Transactions on Smart Grid.

[3]  Jingkun Gao,et al.  A feasibility study of automated plug-load identification from high-frequency measurements , 2015, 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[4]  C.-C. Jay Kuo.  Understanding convolutional neural networks with a mathematical model , 2016, J. Vis. Commun. Image Represent..

[5]  Andrey Brito,et al.  A Technique to provide differential privacy for appliance usage in smart metering , 2016, Inf. Sci..

[6]  A. Grübl,et al.  A Platform for Parallel Operation of VLSI Neural Networks , 2004 .

[7]  Mohammad Yusri Hassan,et al.  A review disaggregation method in Non-intrusive Appliance Load Monitoring , 2016 .

[8]  Karin Strauss,et al.  Accelerating Deep Convolutional Neural Networks Using Specialized Hardware , 2015 .

[9]  Lucas Pereira,et al.  Engineering and deploying a hardware and software platform to collect and label non-intrusive load monitoring datasets , 2017, 2017 Sustainable Internet and ICT for Sustainability (SustainIT).

[10]  Xiaojiang Du,et al.  Achieving differential privacy against non‐intrusive load monitoring in smart grid: A fog computing approach , 2018, Concurr. Comput. Pract. Exp..

[11]  Alistair A. Young,et al.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 2017, MICCAI 2017.

[12]  Gilles Jacquemod,et al.  An innovative non-intrusive load monitoring system for commercial and industrial application , 2012, The 2012 International Conference on Advanced Technologies for Communications.

[13]  Lucas Pereira,et al.  Performance evaluation in non‐intrusive load monitoring: Datasets, metrics, and tools—A review , 2018, Wiley Interdiscip. Rev. Data Min. Knowl. Discov..

[14]  Steven Pigeon,et al.  VIP: an FPGA-based processor for image processing and neural networks , 1996, Proceedings of Fifth International Conference on Microelectronics for Neural Networks.

[15]  Fernando Morgado Dias,et al.  A new solution to the hyperbolic tangent implementation in hardware: polynomial modeling of the fractional exponential part , 2013, Neural Computing and Applications.

[16]  Patrick D. McDaniel,et al.  Protecting consumer privacy from electric load monitoring , 2011, CCS '11.

[17]  Luca Maria Gambardella,et al.  Max-pooling convolutional neural networks for vision-based hand gesture recognition , 2011, 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).

[18]  Abhay Gupta,et al.  Is disaggregation the holy grail of energy efficiency? The case of electricity , 2013 .

[19]  Yann LeCun,et al.  CNP: An FPGA-based processor for Convolutional Networks , 2009, 2009 International Conference on Field Programmable Logic and Applications.

[20]  Lawrence D. Jackel,et al.  A Neurocomputer Board Based on the ANNA Neural Network Chip , 1991, NIPS.

[21]  Chris Develder,et al.  Automated classification of appliances using elliptical fourier descriptors , 2017, 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[22]  Jingkun Gao,et al.  PLAID: a public dataset of high-resoultion electrical appliance measurements for load identification research: demo abstract , 2014, BuildSys@SenSys.

[23]  Fahad Javed,et al.  An Empirical Investigation of V-I Trajectory Based Load Signatures for Non-Intrusive Load Monitoring , 2013, IEEE Transactions on Smart Grid.

[24]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

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

[26]  Christophe Garcia,et al.  Simplifying ConvNets for Fast Learning , 2012, ICANN.

[27]  E. Sackinger,et al.  A system for high-speed pattern recognition and image analysis , 1994, Proceedings of the Fourth International Conference on Microelectronics for Neural Networks and Fuzzy Systems.

[28]  Steven B. Leeb,et al.  FPGA-based spectral envelope preprocessor for power monitoring and control , 2010, 2010 Twenty-Fifth Annual IEEE Applied Power Electronics Conference and Exposition (APEC).

[29]  Chris Develder,et al.  Appliance classification using VI trajectories and convolutional neural networks , 2018 .

[30]  Dimitris Vrakas,et al.  Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation , 2018, Artificial Intelligence Review.

[31]  H. Jacobsen,et al.  WHITED-A Worldwide Household and Industry Transient Energy Data Set , 2016 .

[32]  David Stutz,et al.  Understanding Convolutional Neural Networks , 2014 .

[33]  Fernando Morgado Dias,et al.  Configurable N-fold Hardware Architecture for Convolutional Neural Networks , 2018, 2018 International Conference on Biomedical Engineering and Applications (ICBEA).

[34]  Lawrence D. Jackel,et al.  Application of the ANNA neural network chip to high-speed character recognition , 1992, IEEE Trans. Neural Networks.

[35]  Kunihiko Fukushima,et al.  Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position , 1982, Pattern Recognit..

[36]  Suhono Harso Supangkat,et al.  Appliances identification method of non-intrusive load monitoring based on load signature of V-I trajectory , 2015, 2015 International Conference on Information Technology Systems and Innovation (ICITSI).

[37]  Li Xu,et al.  On Vectorization of Deep Convolutional Neural Networks for Vision Tasks , 2015, AAAI.

[38]  Joseph R. Herkert,et al.  Societal implications of the emerging smart grid , 2012, CACM.

[39]  Jason Cong,et al.  Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks , 2015, FPGA.

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

[41]  Geoffrey E. Hinton,et al.  Gated Softmax Classification , 2010, NIPS.

[42]  Bin Yang,et al.  Neural Network Ensembles to Real-time Identification of Plug-level Appliance Measurements , 2018, ArXiv.

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

[44]  Darío Baptista,et al.  Low-resource hardware implementation of the hyperbolic tangent for artificial neural networks , 2013, Neural Computing and Applications.

[45]  H. Y. Lam,et al.  A Novel Method to Construct Taxonomy Electrical Appliances Based on Load Signaturesof , 2007, IEEE Transactions on Consumer Electronics.