# Improved Appliance Classification in Non-Intrusive Load Monitoring Using Weighted Recurrence Graph and Convolutional Neural Networks

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## Abstract

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## 1. Introduction

- We present a recurrence graph feature representation that gives a few more values (WRG) instead of the binary output, which improves the robustness of appliance recognition. The WRG representation for activation current and voltage not only enhances appliance classification performance but also guarantees the appliance feature’s uniqueness, which is highly desirable for generalization purposes.
- We present a novel pre-processing procedure for extracting steady-state cycle activation current from current and voltage measurements. The pre-processing method ensures that the selected activation current is not a transient signal.
- We conduct evaluations on three sub-metered public datasets and comparing with the V–I image, which is its most direct competitor. We also conduct an empirical investigation on how different parameters of the proposed WRG influence classification performance.

## 2. Proposed Methods

#### 2.1. Feature Extraction and Pre-Processing

Algorithm 1: Feature pre-processing |

Result: ${\mathbf{i}}^{j},{\mathbf{v}}^{j}$Data: ${\mathbf{i}}_{1}^{N},{\mathbf{v}}_{1}^{N}$Get voltage zero crossings: ${\mathbf{zc}}_{v}$; |

#### 2.2. Weighted Recurrence Plot (WRG)

#### 2.3. Classifier and Training Procedure

## 3. Experimental Design

#### 3.1. Datasets

#### 3.2. Evaluation Metrics

#### 3.3. Experimental Description

## 4. Results and Discussion

#### 4.1. Objective 1: WRG Analysis

#### 4.2. Objective 2: Comparison against V–I Image Method

## 5. Conclusions and Future Work Directions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**Block diagram of the proposed approach. It consist of the Feature extraction and pre-processing, WRG generation and the CNN classifier blocks.

**Figure 2.**Extraction of an activation signal from current and voltage measurements. The green color is the steady-state signal before the event where the steady-state signal after the event is shown in red: (

**a**) Voltage waveforms before and after an event; (

**b**) Activation voltage; (

**c**) Current waveforms before and after events; (

**d**) Activation current.

**Figure 3.**Illustration of dimension reduction with PAA for different embedding w: (

**a**) The original activation current before dimension reduction; (

**b**) The activation current after PAA with $w=50$. The generated signal resembles the original activation current before PAA; (

**c**) The activation current after PAA with $w=10$. There is a loss of information on the generated signal.

**Figure 4.**Generation of distance similarity matrix and RGs for a vacuum cleaner activation current in PLAID dataset: (

**a**) Distance similarity matrix ${D}_{w\times w}$; (

**b**) WRG matrix $WR{G}_{w\times w}$; (

**c**) RG matrix $R{G}_{w\times w}$.

**Figure 5.**Generation of V–I image from Microwave activation current and voltage in the PLAID dataset: (

**a**) Activation current; (

**b**) Activation voltage; (

**c**) V–I trajectory; (

**d**) Generated V–I image.

**Figure 6.**Impact of WRG parameters in the measured performance: (

**a**) Impact of $\lambda $ for different value of $\delta $ on the COOLL dataset; (

**b**) Impact of $\lambda $ for different value of $\delta $ on the PLAID dataset; (

**c**) The relationship between w and $MCC$ score for COOLL and PLAID dataset; (

**d**) The relationship between w and training time on the PLAID and COOLL dataset.

**Figure 7.**The ${F}_{1}$ macro (%) matrix per appliance and confusion matrix for the COOLL dataset: (

**a**) ${F}_{1}$ macro (%) for V–I and WRG; (

**b**) V–I confusion matrix; (

**c**) WRG confusion matrix.

**Figure 8.**The ${F}_{1}$ macro (%) matrix per appliance and confusion matrix for the PLAID dataset: (

**a**) ${F}_{1}$ macro (%) for VI and WRG; (

**b**) V–I confusion matrix; (

**c**) WRG confusion matrix; AC = air conditioning, CFL = compact fluorescent lamp, ILB = incandescent light bulb.

**Figure 9.**The The ${F}_{1}$ macro and confusion matrix for the WHITED dataset: (

**a**) The ${F}_{1}$ macro (%) for the WHITED dataset with V–I and WRG feature representation; (

**b**) WRG confusion matrix.

Parameter | COOLL | WHITED | PLAID |
---|---|---|---|

$\lambda =\frac{1}{\u03f5}$ | ${10}^{3}$ | ${10}^{3}$ | ${10}^{1}$ |

$\delta $ | 50 | 50 | 20 |

w | 50 | 50 | 50 |

Data | Method | Metrics | ||
---|---|---|---|---|

MCC | F1 | ZL | ||

COOLL | RG | $0.98$ | $9899$ | $190$ |

WRG | $1.00$ | $99.86$ | $0.12$ | |

PLAID | RG | $0.91$ | $88.18$ | $8.18$ |

WRG | $0.97$ | $94.35$ | $2.98$ |

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## Share and Cite

**MDPI and ACS Style**

Faustine, A.; Pereira, L.
Improved Appliance Classification in Non-Intrusive Load Monitoring Using Weighted Recurrence Graph and Convolutional Neural Networks. *Energies* **2020**, *13*, 3374.
https://doi.org/10.3390/en13133374

**AMA Style**

Faustine A, Pereira L.
Improved Appliance Classification in Non-Intrusive Load Monitoring Using Weighted Recurrence Graph and Convolutional Neural Networks. *Energies*. 2020; 13(13):3374.
https://doi.org/10.3390/en13133374

**Chicago/Turabian Style**

Faustine, Anthony, and Lucas Pereira.
2020. "Improved Appliance Classification in Non-Intrusive Load Monitoring Using Weighted Recurrence Graph and Convolutional Neural Networks" *Energies* 13, no. 13: 3374.
https://doi.org/10.3390/en13133374