An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals. Issue 1 (21st September 2020)

• Record Type: Journal Article
• Authors: Himeur, Yassine; Alsalemi, Abdullah; Bensaali, Faycal; Amira, Abbes
• Is Part Of: International journal of intelligent systems. Volume 36:Issue 1(2021)
• Page Start: 72
• Page End: 93
• Publication Date: 2020-09-21
• Subjects: appliance identification -- classification -- feature extraction -- nonintrusive load monitoring -- phase encoding; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22292
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: Nonintrusive load monitoring (NILM) is the de facto technique for extracting device‐level power consumption fingerprints at (almost) no cost from only aggregated mains readings. Specifically, there is no need to install an individual meter for each appliance. However, a robust NILM system should incorporate a precise appliance identification module that can effectively discriminate between various devices. In this context, this paper proposes a powerful method to extract accurate power fingerprints for electrical appliance identification. Rather than relying solely on time‐domain (TD) analysis, this framework abstracts the phase encoding of the TD description of power signals using a two‐dimensional (2D) representation. This allows mapping power trajectories to a novel 2D binary representation space, and then performing a histogramming process after converting binary codes to new decimal representations. This yields the final histogram of 2D phase encoding of power signals, namely, 2D‐PEP. An empirical performance evaluation conducted with three realistic power consumption databases collected at distinct resolutions indicates that the proposed 2D‐PEP descriptor achieves outperformance for appliance identification in comparison with other recent techniques. Accordingly, high identification accuracies are attained on the GREEND, UK‐DALE, and WHITED data sets, where 99.54%, 98.78%, and 100% rates have been achieved, respectively, using the proposed 2D‐PEP descriptor.