On the Use of Concentrated Time-Frequency Representations as Input to a Deep Convolutional Neural Network: Application to Non Intrusive Load Monitoring

Sarra Houidi (1) , Dominique Fourer (1) , François Auger (2)

1 IBISC - Informatique, BioInformatique, Systèmes Complexes
2 IREENA - Institut de Recherche en Energie Electrique de Nantes Atlantique EA4642

Since decades past, time-frequency (TF) analysis has demonstrated its capability to efficiently handle non-stationary multi-component signals which are ubiquitous in a large number of applications. TF analysis us allows to estimate physics-related meaningful parameters (e.g., F 0 , group delay, etc.) and can provide sparse signal representations when a suitable tuning of the method parameters is used. On another hand, deep learning with Convolutional Neural Networks (CNN) is the current state-of-the-art approach for pattern recognition and allows us to automatically extract relevant signal features despite the fact that the trained models can suffer from a lack of interpretability. Hence, this paper proposes to combine together these two approaches to take benefit of their respective advantages and addresses non-intrusive load monitoring (NILM) which consists of identifying a home electrical appliance (HEA) from its measured energy consumption signal as a "toy" problem. This study investigates the role of the TF representation when synchrosqueezed or not, used as the input of a 2D CNN applied to a pattern recognition task. We also propose a solution for interpreting the information conveyed by the trained CNN through different neural architecture by establishing a link with our previously proposed "handcrafted" interpretable features thanks to the layer-wise relevant propagation (LRP) method. Our experiments on the publicly available PLAID dataset show excellent appliance recognition results (accuracy above 97%) using the suitable TF representation and allow an interpretation of the trained model.

Keywords: non-intrusive load monitoring (NILM); time-frequency representation (TFR); deep learning; convolutional neural network (CNN); synchrosqueezing; Layer wise relevance propagation (LRP)

Domains: Signal and Image Processing; Electric power; Machine Learning [cs.LG]

Sarra Houidi, Dominique Fourer, François Auger. On the Use of Concentrated Time-Frequency Representations as Input to a Deep Convolutional Neural Network: Application to Non Intrusive Load Monitoring. Entropy, 2020, 22 (9), pp.911. ⟨10.3390/e22090911⟩. ⟨hal-02917749⟩