Recurrence Plot Spacial Pyramid Pooling Network for Appliance Identification in Non-Intrusive Load Monitoring

  • Parameter free Non-intrusive Load Monitoring (NILM) algorithms are a major step toward real-world NILM scenarios. The identification of appliances is the key element in NILM. The task consists of identification of the appliance category and its current state. In this paper, we present a param- eter free appliance identification algorithm for NILM using a 2D representation of time series known as unthresholded Recurrence Plots (RP) for appliance category identification. One cycle of voltage and current (V-I trajectory) are transformed into a RP and classified using a Spacial Pyramid Pooling Convolutional Neural Network architecture. The performance of our approach is evaluated on the three public datasets COOLL, PLAID and WHITEDv1.1 and compared to previous publications. We show that compared to other approaches using our architecture no initial parameters have to be manually tuned for each specific dataset.

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Metadaten
Author:Marc Wenninger, Sebastian P. Bayerl, Andreas Maier, Jochen SchmidtORCiD
URL:https://doi.org/10.1109/ICMLA52953.2021.00025
Parent Title (English):2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)
Document Type:Conference Proceeding
Language:English
Publication Year:2021
Tag:NILM; Recurrence Plot; V-I trajectory
Page Number:8
First Page:108
Last Page:115
Peer reviewed:Ja
faculties / departments:Fakultät für Informatik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke