# Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

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## Abstract

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## 1. Introduction

- We first demonstrate that for aggregated measurements, the use of activation current as an input feature offers improved performance compared to the regularly used V-I binary image feature.
- Second, we apply the Fryze power theory and Euclidean distance matrix as pre-processing steps for the multi-label classifier. This pre-processing step improves the appliance feature’s uniqueness and enhances the performance of the multi-label classifier.
- Third, we propose a CNN multi-label classifier that uses softmax activation to capture the relations between multiple appliances implicitly.
- Fourth, we conduct an experimental evaluation of the proposed approach on an aggregated public dataset and compare the general and per-appliance performances. We also provide an in-depth error analysis and identified three types of errors for multi-label appliance recognition in NILM. Finally, a complexity analysis of the proposed approach method is also presented.

## 2. Related Works

## 3. Proposed Methods

#### 3.1. Feature Extraction from Aggregate Measurements

#### 3.2. Feature Pre-Processing

#### 3.3. Multi-Label Modeling

## 4. Evaluation Methodology

#### 4.1. Dataset

#### 4.2. Performance Metrics

#### 4.3. Experiment Description

## 5. Results and Discussion

#### 5.1. Comparison with Baseline

#### 5.2. Error Analysis

#### 5.3. Complexity Analysis

#### 5.4. Comparison with State-of-the-Art Methods

## 6. Conclusions and Future Work Directions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**The block diagram of the proposed method. The dotted block is the pre-processing block where PAA stand for Piecewise Aggregate Approximation, a dimension reduction method for high-dimensional time series signal.

**Figure 2.**(

**a**) Aggregated current signal for different events. (

**b**) Twenty cycles of current after an event. (

**c**) Extracted activation currents for different events. The activation current of the event at time $\mathbf{i}$ is the summation of the activation current for the all running appliances.

**Figure 3.**Activation voltage $v\left(t\right)$ for different appliances in the PLAID dataset. The voltage has an almost identical pattern for all the appliances.

**Figure 4.**Normalized source current $i\left(t\right)$ and their respective active $i{\left(t\right)}_{a}$ and reactive components $i{\left(t\right)}_{f}$ after applying Fryze power theory. The current is normalized for visualization purposes.

**Figure 5.**Currents and distance matrix when Compact Fluorescent Lamp (CFL) and laptop charger are active. (

**a**) Source current $i\left(t\right)$. (

**b**) Active current ${i}_{a}\left(t\right)$. (

**c**) Non-active current ${i}_{a}\left(t\right)$. (

**d**) Distance matrix for source current. (

**e**) Distance matrix for active current (

**f**) Distance matrix for non-active current.

**Figure 6.**Block diagram of the Convolutional Neural Network (CNN) multi-label classifier. It consists of a CNN encoder to learn feature representation from the input feature, and the output layer to produce the predicted labels.

**Figure 7.**(

**a**) Active appliances distributions. (

**b**) Appliances distribution on the extracted 1154 activations. The soldering iron has large number of activations because it has two start-up events.

**Figure 8.**$\mathrm{ma}{F}_{1}$ score performance comparison between the proposed CNN model and the two baselines for different inputs features: (

**a**) Comparison between voltage-current (V-I) binary image and current activation features; (

**b**) Comparison between the activation current based features.

**Figure 9.**Prediction comparison for different feature representations with the proposed CNN multilabel classifier. (

**a**) Action current. (

**b**) Decomposed current. (

**c**) V-I image. (

**d**) Distance matrix.

**Figure 10.**Per-appliance $\mathrm{eb}{F}_{1}$ score on PLAID dataset. AC = air conditioning, CFL = compact fluorescent lamp, ILB = incandescent light bulb. (

**a**) Multi-label k-nearest-neighbor (MLkNN) (

**b**) CNN.

**Figure 11.**(

**a**) Distributions of type errors the model makes. (

**b**) Number of correct predictions for single, double and triple activations.

**Figure 12.**(

**a**) Distributions of type errors the model makes. (

**b**) Number of correct predictions for single, double and triple activations.

Approach | Learning Strategy | Model | Dataset | Sampling Frequency | Results (Metric) |
---|---|---|---|---|---|

De Baets et al. [19] | single | CNN | PLAID [30] | High | 88.0% (${F}_{1}$ macro) |

Faustine et al. [13] | single | CNN | PLAID [30] | High | 97.77% (${F}_{1}$ macro) |

Tabatabaei et al. [26] | multi | MLkNN | REDD-House1 [50] | Low | 61.90% (${F}_{1}$ macro) |

Lai et al. [49] | multi | SVM/GMM | Private | - | 90.72% (Accuracy) |

Yang et al. [23] | multi | FCNN | UK-DALE-house 1 [51] | Low | 93.8% (${F}_{1}$ score) |

Nalmpantis and Vrakas [37] | multi | TCNN | UK-DALE-house 1 [51] | Low | 92.5% (${F}_{1}$ score) |

Proposed approach | multi | CNN | PLAID [30] | High | 94.0% (${F}_{1}$ score) |

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## Share and Cite

**MDPI and ACS Style**

Faustine, A.; Pereira, L.
Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network. *Energies* **2020**, *13*, 4154.
https://doi.org/10.3390/en13164154

**AMA Style**

Faustine A, Pereira L.
Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network. *Energies*. 2020; 13(16):4154.
https://doi.org/10.3390/en13164154

**Chicago/Turabian Style**

Faustine, Anthony, and Lucas Pereira.
2020. "Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network" *Energies* 13, no. 16: 4154.
https://doi.org/10.3390/en13164154