A Highly Accurate NILM: With an Electro-Spectral Space That Best Fits Algorithm’s National Deployment Requirements

Netzah Calamaro, Moshe Donko and Doron Shmilovitz
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Netzah Calamaro: Faculty of Electrical and Electronics Engineering, Tel-Aviv University, Tel-Aviv 39040, Israel
Moshe Donko: Faculty of Electrical and Electronics Engineering, Tel-Aviv University, Tel-Aviv 39040, Israel
Doron Shmilovitz: Faculty of Electrical and Electronics Engineering, Tel-Aviv University, Tel-Aviv 39040, Israel

Energies, 2021, vol. 14, issue 21, 1-37

Abstract: The central problems of some of the existing Non-Intrusive Load Monitoring (NILM) algorithms are indicated as: (1) higher required electrical device identification accuracy; (2) the fact that they enable training over a larger device count; and (3) their ability to be trained faster, limiting them from usage in industrial premises and external grids due to their sensitivity to various device types found in residential premises. The algorithm accuracy is higher compared to previous work and is capable of training over at least thirteen electrical devices collaboratively, a number that could be much higher if such a dataset is generated. The algorithm trains the data around 1.8 × 10 8 faster due to a higher sampling rate. These improvements potentially enable the algorithm to be suitable for future “grids and industrial premises load identification” systems. The algorithm builds on new principles: an electro-spectral features preprocessor, a faster waveform sampling sensor, a shorter required duration for the recorded data set, and the use of current waveforms vs. energy load profile, as was the case in previous NILM algorithms. Since the algorithm is intended for operation in any industrial premises or grid location, fast training is required. Known classification algorithms are comparatively trained using the proposed preprocessor over residential datasets, and in addition, the algorithm is compared to five known low-sampling NILM rate algorithms. The proposed spectral algorithm achieved 98% accuracy in terms of device identification over two international datasets, which is higher than the usual success of NILM algorithms.

Keywords: KDE—kernel density estimation; GMM—Gaussian mixture model; KNN—K-nearest neighbor; NILM—nonintrusive load monitoring; PCA—principal component analysis; NIS—network information system; RNN—recurrent neural network; SGD—stochastic gradient descent; DSO—distributed system operator; E-V—electric vehicle; P-V—photo-voltaic; HGL—harmonic generating load (inspired from current’s physical components theory) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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