Wavenilm: A Causal Neural Network for Power Disaggregation from the Complex Power Signal
TL
TL;DR
This work presents a causal 1-D convolutional neural network inspired by WaveNet for NILM on low-frequency data and demonstrates that using all four components available in a popular NilM dataset achieves faster convergence and higher performance than state-of-the-art results for the same dataset.
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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Exploiting the Reactive Power in Deep Neural Models for Non-Intrusive Load Monitoring
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