Wavenilm: A causal neural network for power disaggregation from the complex power signal

Alon Harell; Stephen Makonin; Ivan V. Bajić

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arXiv · 1902.08736

Wavenilm: A causal neural network for power disaggregation from the complex power signal

Alon Harell·Stephen Makonin·Ivan V. Bajić

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Abstract

Non-intrusive load monitoring (NILM) helps meet energy conservation goals by estimating individual appliance power usage from a single aggregate measurement. Deep neural networks have become increasingly popular in attempting to solve NILM problems; however, many of them are not causal which is important for real-time application. We present a causal 1-D convolutional neural network inspired by WaveNet for NILM on low-frequency data. We also study using various components of the complex power signal for NILM, and demonstrate that using all four components available in a popular NILM dataset (current, active power, reactive power, and apparent power) we achieve faster convergence and higher performance than state-of-the-art results for the same dataset.

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BibTeX RIS

Alon Harell, Stephen Makonin, Ivan V. Bajić. 2019-02-23. Wavenilm: A causal neural network for power disaggregation from the complex power signal. https://arxiv.org/abs/1902.08736

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