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Energies 2018, 11(9), 2460; https://doi.org/10.3390/en11092460

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

1
Instituto Superior Tecnico, Universidade de Lisboa, 1649-001 Lisbon, Portugal
2
M-ITI—Madeira Interactive Technologies Institute, 9020-105 Funchal, Portugal
3
INESC-ID Instituto de Engenharia de Sistemas e Computadores—Investigação e Desenvolvimento, DEEC, 1000-029 Lisbon, Portugal
4
Ciências Exatas e Engenharia, UMa-University of Madeira, 9020-105 Funchal, Portugal
*
Author to whom correspondence should be addressed.
Received: 26 July 2018 / Revised: 8 September 2018 / Accepted: 10 September 2018 / Published: 17 September 2018
(This article belongs to the Special Issue 10 Years Energies - Horizon 2028)
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Abstract

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. View Full-Text
Keywords: non-intrusive load monitoring; convolution neural network; V-I trajectory; hardware classifier; FPGA non-intrusive load monitoring; convolution neural network; V-I trajectory; hardware classifier; FPGA
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Baptista, D.; Mostafa, S.S.; Pereira, L.; Sousa, L.; Morgado-Dias, F. Implementation Strategy of Convolution Neural Networks on Field Programmable Gate Arrays for Appliance Classification Using the Voltage and Current (V-I) Trajectory. Energies 2018, 11, 2460.

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