Convolutional neural networks for robust angular measurement with xMR sensor arrays

Meier, Phil; Rohrmann, Kris; Sandner, Marvin; Streitenberger, M.; Prochaska, Marcus

doi:10.1109/i2mtc.2019.8827051

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…This system is capable of acquiring and processing the overall user current. The proposed solution is a DL-based NILM system, which adopts a particular type of ANN, namely, the convolutional neural network (CNN) [20], [21], [22]. This CNN is suitable for processing complex inputs such as multidimensional arrays.…”

Section: Introductionmentioning

confidence: 99%

A New Convolutional Neural Network-Based System for NILM Applications

Ciancetta

Bucci

Fiorucci

et al. 2021

IEEE Trans. Instrum. Meas.

102

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Electrical load planning and demand response programs are often based on the analysis of individual load level measurements obtained from houses or buildings. The identification of individual appliances' power consumption is essential, since it allows improvements, which can reduce appliances' power consumption. In this paper, the problem of identifying the electrical loads connected to a house, starting from the total electric current measurement, is investigated. The proposed system is capable of extracting the energy demand of each individual device using a non-intrusive load monitoring (NILM) technique. A NILM algorithm based on a convolutional neural network is proposed. The proposed algorithm allows simultaneous detection and classification of events without having to perform double processing. As a result, the calculation times can be reduced. Another important advantage is that only the acquisition of current is required. The proposed measurement system is also described in this paper. Measurements are conducted using a test system, which is capable of generating the electrical loads found on a typical house. The most important experimental results are also included and discussed in the paper.

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Section: Introductionmentioning

confidence: 99%