Deep Learning-Based Energy Disaggregation and On/Off Detection of Household Appliances

Abstract

Energy disaggregation, a.k.a. Non-Intrusive Load Monitoring, aims to separate the energy consumption of individual appliances from the readings of a mains power meter measuring the total energy consumption of, e.g., a whole house. Energy consumption of individual appliances can be useful in many applications, e.g., providing appliance-level feedback to the end users to help them understand their energy consumption and ultimately save energy. Recently, with the availability of large-scale energy consumption datasets, various neural network models such as convolutional neural networks and recurrent neural networks have been investigated to solve the energy disaggregation problem. Neural network models can learn complex patterns from large amounts of data and have been shown to outperform the traditional machine learning methods such as variants of hidden Markov models. However, current neural network methods for energy disaggregation are either computational expensive or are not capable of handling long-term dependencies. In this article, we investigate the application of the recently developed WaveNet models for the task of energy disaggregation. Based on a real-world energy dataset collected from 20 households over 2 years, we show that WaveNet models outperforms the state-of-the-art deep learning methods proposed in the literature for energy disaggregation in terms of both error measures and computational cost. On the basis of energy disaggregation, we then investigate the performance of two deep-learning based frameworks for the task of on/off detection which aims at estimating whether an appliance is in operation or not. The first framework obtains the on/off states of an appliance by binarising the predictions of a regression model trained for energy disaggregation, while the second framework obtains the on/off states of an appliance by directly training a binary classifier with binarised energy readings of the appliance serving as the target values. Based on the same dataset, we show that for the task of on/off detection the second framework, i.e., directly training a binary classifier, achieves better performance in terms of F1 score.

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“…1 [6], namely the WaveNet-based algorithm of [9]. Good performance was demonstrated in [9] on household appliances, which are very distinct to those observed in milk production loads. One of the model's major strengths is that it has a large field of view, produces concatenated and processed outputs from multiple layers in the network, each with different field of view, which makes this model available to recognise patterns on multiple scales.…”

“…We leverage on one of the best performing low frequency deep learning based regression networks as highlighted in Fig. 1 [6], namely the WaveNet-based algorithm of [9]. Good performance was demonstrated in [9] on household appliances, which are very distinct to those observed in milk production loads.…”

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“…Various studies have been presented in the literature to circumvent these challenges and apply the NILM techniques effectively. Hidden Markov Models (HM) and Factorial Markov Models (FHM) [7][8][9][10] are also widely utilised [2,11]. However, one of the problems with these techniques is the requirement of pre-existing knowledge about the number of appliances, which is assumed to be fixed.…”