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

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## Abstract

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## 1. Introduction

## 2. Background and Related Work

#### 2.1. V-I Shapes and NILM

#### 2.2. Convolution Neural Network Backgroud

#### 2.3. FPGA Implementations of NILM and CNNs

## 3. Materials and Methods

#### 3.1. Dataset

#### 3.2. Data Pre-Processing

#### 3.3. CNN for Appliance Classification

#### 3.4. CNN Implementation on FPGA

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#### 3.5. Evaluation Metrics

#### 3.6. Power and Temperature Effects on the FPGA

## 4. Results and Discussion

#### 4.1. Validation of the CNN Classifier According to Window Sizes

#### 4.2. Performance and Cost

#### 4.3. Comparison Results

## 5. Conclusion and Future Work Directions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**V-I trajectory of 11 appliances for three different window size. These pictures are created for visual purpose only actual input is a 50 × 50.

**Figure 3.**Design implemented. C: Calculation of the convolution; P: Calculation of the pooling; F: Calculation of the full output; K: Convolution Kernel; b: Bias; W: Weight.

**Figure 4.**Demonstrative diagram of the parallelism of the implementation: (

**a**) multiplication/accumulation for convolving with a kernel $k\in {\mathbb{R}}^{2\times 2}$; (

**b**) pooling assuming a filter belonging to ${\mathbb{R}}^{2\times 2}$.

**Figure 5.**F-score values for each house from database plug load appliance identification dataset (PLAID) 1: x axis indicating the different periods and y axis is the F-score in percentage (%).

**Figure 6.**F-score values for each appliance from database PLAID 1: x axis indicating the different appliance and y axis is the F-score in percentage (%).

**Figure 7.**F-score values for each house from database PLAID 2: x axis indicating the different periods and y axis is the F-score in percentage (%).

**Figure 8.**F-score values for each appliance from database PLAID 2: x axis indicating the different appliance and y axis is the F-score in percentage (%).

**Figure 9.**F-score values for PLAID 1, PLAID2 and weighted F-macro-score for PLAID: x axis indicating the different periods and y axis is the F-score in percentage (%).

**Figure 10.**Box and whisker plot for PLAID 1: x axis indicating the different appliances and y axis is the F-score in percentage (%).

**Figure 11.**Box and whisker plot for PLAID 2: x axis indicating the different appliances and y axis is the F-score in percentage (%).

Layer | Kernel/Pooling Window | Layer Size |
---|---|---|

Input | - | [email protected] |

Convolution—stride 1 (C1) | [[email protected]] | [email protected] |

Pooling—stride 2 (P1) | [[email protected]] | [email protected] |

Convolution—stride 1 (C2) | [[email protected]] | [email protected] |

Pooling—stride 2 (P2) | [[email protected]] | [email protected] |

Convolution—stride 1 (C3) | [[email protected]] | [email protected] |

Full out (F1) | - | 11 |

**Table 2.**Performance, cost and power consumption of the field programmable gate array (FPGA) for the implemented CNN.

Resources | LUT | 47.25% (25,138 of 53,200) |

LUTRAM | 1.41% (246 of 17400) | |

FF | 13.05% (13,884 of 10,6400) | |

BRAM | 36.43% (51 of 140) | |

DSP | 71.82% (158 of 220) | |

BUFG | 3.13% (1 of 32) | |

Latency (ms) | ≅ 5.7 | |

Power | Dynamic (W) | 1.701 |

Device Static (W) | 0.167 | |

Total On-Chip Power (W) | 1.868 | |

Junction Temperature (°C) | 46.5 | |

Thermal Margin (°C) | 38.5 | |

Effective thermal resistance to air (°C/W) | 11.5 |

Appliance | Convolutional Neural Networks [9] | Neural Network Ensembles [17] | Our Classifier | |
---|---|---|---|---|

PLAID 1 | PLAID 1 | PLAID 1 | PLAID 2 | |

CFL | 95.60% | 69.8% | 90.86% | 83.96% |

Fridge | 50.93% | 96.9% | 58.91% | 54.32% |

Hairdryer | 79.76% | 74.1% | 84.70% | 68.40% |

Microwave | 93.14% | 74.0% | 86.98% | 76.54% |

AC | 46.65% | 92.6% | 61.20% | 57.55% |

Laptop | 97.94% | 77.4% | 88.01% | 71.01% |

Vacuum | 97.91% | 88.2% | 97.55% | 94.94% |

ILB | 80.58% | 95.6% | 84.83% | 61.63% |

Fan | 60.12% | 98.6% | 54.18% | 30.04% |

WM | 68.82% | 96.1% | 80.62% | 57.02% |

Heater | 82.23% | 89.4% | 71.92% | 70.67% |

Total | 77.61% | 86.61% | 78.16% | 66.01% |

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## Share and Cite

**MDPI and ACS Style**

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.
https://doi.org/10.3390/en11092460

**AMA Style**

Baptista D, Mostafa SS, 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(9):2460.
https://doi.org/10.3390/en11092460

**Chicago/Turabian Style**

Baptista, Darío, Sheikh Shanawaz Mostafa, Lucas Pereira, Leonel Sousa, and Fernando Morgado-Dias. 2018. "Implementation Strategy of Convolution Neural Networks on Field Programmable Gate Arrays for Appliance Classification Using the Voltage and Current (V-I) Trajectory" *Energies* 11, no. 9: 2460.
https://doi.org/10.3390/en11092460