On the Bayesian optimization and robustness of event detection methods in NILM

A basic but crucial step to increase efficiency and save energy in residential settings is to have an accurate view of energy consumption. To monitor residential energy consumption cost-effectively, i.e., without relying on per-device monitoring equipment, non-intrusive load monitoring (NILM) provides an elegant solution. The aim of NILM is to disaggregate the total power consumption (as measured, e.g., by smart meters at the grid connection point of the household) into individual devices' power consumption, using machine learning techniques. An essential building block of NILM is event detection: detecting when appliances are switched on or off. Current state-of-the-art methods face two open issues. First, they are typically not robust to differences in base load power consumption and secondly, they require extensive parameter optimization. In this paper, both problems are addressed. First two novel and robust algorithms are proposed: a modified version of the chi-squared goodness-of-fit (x(2) GOF) test and an event detection method based on cepstrum smoothing. Then, a workflow using surrogate-based optimization (SBO) to efficiently tune these methods is introduced. Benchmarking on the BLUED dataset shows that both suggested algorithms outperform the standard x2 GOF test for traces with a higher base load and that they can be optimized efficiently using SBO. (C) 2017 Elsevier B.V. All rights reserved

Appliance classification using VI trajectories and convolutional neural networks

Non-intrusive load monitoring methods aim to disaggregate the total power consumption of a household into individual appliances by analysing changes in the voltage and current measured at the grid connection point of the household. The goal is to identify the active appliances, based on their unique fingerprint. An informative characteristic to attain this goal is the voltage-current trajectory. In this paper, a weighted pixelated image of the voltage-current trajectory is used as input data for a deep learning method: a convolutional neural network that will automatically extract key features for appliance classification. The macro-average F-measure is 77.60% for the PLAID dataset and 75.46% for the WHITED dataset. (C) 2017 Elsevier B.V. All rights reserved

VI-based appliance classification using aggregated power consumption data

Non-intrusive load monitoring detects active appliances in a household (and their power consumption) from measuring the aggregated power at just one point in that household. Our previous works focused on classifying a single appliance, assuming that the voltage and current trace could be isolated from an aggregated signal by considering the difference in current before and after the event. In this paper, we show that this assumption holds and that it is a viable approach in practice. We experimentally validate this for two classification methods we proposed earlier: (1) random forests using elliptical Fourier descriptors of the appliances' VI trajectories and (2) convolutional neural networks using the appliances' VI images. We benchmark these approaches on the aggregated data from the 2018 version of PLAID. We obtain, respectively for each of these classifiers, a maximal F-macro-measure of 85.31% and 87.95 %. We also show that using submetered data for training does not improve the performance