Filtering in non-Intrusive load monitoring

Being able to track appliances energy usage without the need of sensors can help occupants reduce their energy consumption. Non-intrusive load monitoring (NILM) is one name for this topic. One of the hardest problems NILM faces is the ability to run unsupervised – discovering appliances without prior knowledge – and to run independent of the differences in appliance mixes and operational characteristics found in various countries and regions. This thesis showcases two filters that are used to denoise power signals, which results in better clustering accuracy for NILM event based methods. Both filters show to outperform a state-of-the-art denoising filter, in terms of run-time. A fully unsupervised NILM solution is presented, the algorithm is based on a hybrid knapsack problem with a Gaussian mixture model. Finally, a novel metric is developed to measure NILM disaggregation performance. The metric shows to be robust under a set of fundamental test cases

Non-intrusive load monitoring solutions for low- and very low-rate granularity

Strathclyde theses - ask staff. Thesis no. : T15573Large-scale smart energy metering deployment worldwide and the integration of smart meters within the smart grid are enabling two-way communication between the consumer and energy network, thus ensuring an improved response to demand. Energy disaggregation or non-intrusive load monitoring (NILM), namely disaggregation of the total metered electricity consumption down to individual appliances using purely algorithmic tools, is gaining popularity as an added-value that makes the most of meter data.In this thesis, the first contribution tackles low-rate NILM problem by proposing an approach based on graph signal processing (GSP) that does not require any training.Note that Low-rate NILM refers to NILM of active power measurements only, at rates from 1 second to 1 minute. Adaptive thresholding, signal clustering and pattern matching are implemented via GSP concepts and applied to the NILM problem. Then for further demonstration of GSP potential, GSP concepts are applied at both, physical signal level via graph-based filtering and data level, via effective semi-supervised GSP-based feature matching. The proposed GSP-based NILM-improving methods are generic and can be used to improve the results of various event-based NILM approaches. NILM solutions for very low data rates (15-60 min) cannot leverage on low to highrates NILM approaches. Therefore, the third contribution of this thesis comprises three very low-rate load disaggregation solutions, based on supervised (i) K-nearest neighbours relying on features such as statistical measures of the energy signal, time usage profile of appliances and reactive power consumption (if available); unsupervised(ii) optimisation performing minimisation of error between aggregate and the sum of estimated individual loads, where energy consumed by always-on load is heuristically estimated prior to further disaggregation and appliance models are built only by manufacturer information; and (iii) GSP as a variant of aforementioned GSP-based solution proposed for low-rate load disaggregation, with an additional graph of time-of-day information.Large-scale smart energy metering deployment worldwide and the integration of smart meters within the smart grid are enabling two-way communication between the consumer and energy network, thus ensuring an improved response to demand. Energy disaggregation or non-intrusive load monitoring (NILM), namely disaggregation of the total metered electricity consumption down to individual appliances using purely algorithmic tools, is gaining popularity as an added-value that makes the most of meter data.In this thesis, the first contribution tackles low-rate NILM problem by proposing an approach based on graph signal processing (GSP) that does not require any training.Note that Low-rate NILM refers to NILM of active power measurements only, at rates from 1 second to 1 minute. Adaptive thresholding, signal clustering and pattern matching are implemented via GSP concepts and applied to the NILM problem. Then for further demonstration of GSP potential, GSP concepts are applied at both, physical signal level via graph-based filtering and data level, via effective semi-supervised GSP-based feature matching. The proposed GSP-based NILM-improving methods are generic and can be used to improve the results of various event-based NILM approaches. NILM solutions for very low data rates (15-60 min) cannot leverage on low to highrates NILM approaches. Therefore, the third contribution of this thesis comprises three very low-rate load disaggregation solutions, based on supervised (i) K-nearest neighbours relying on features such as statistical measures of the energy signal, time usage profile of appliances and reactive power consumption (if available); unsupervised(ii) optimisation performing minimisation of error between aggregate and the sum of estimated individual loads, where energy consumed by always-on load is heuristically estimated prior to further disaggregation and appliance models are built only by manufacturer information; and (iii) GSP as a variant of aforementioned GSP-based solution proposed for low-rate load disaggregation, with an additional graph of time-of-day information

A Cloud-based On-line Disaggregation Algorithm for Home Appliance Loads

In this work, we address the problem of providing fast and on-line households appliance load detection in a non-intrusive way from aggregate electric energy consumption data. Enabling on-line load detection is a relevant research problem as it can unlock new grid services such as demand-side management and raises interactivity in energy awareness possibly leading to more green behaviours. To this purpose, we propose an On-line-NILM (Non-Intrusive Load Monitoring) machine learning algorithm combining two methodologies: i) Unsupervised event-based profiling and ii) Markov chain appliance load modelling. The event-based part performs event detection through contiguous and transient data segments, events clustering and matching. The resulting features are used to build household-specific appliance models from generic appliance models. Disaggregation is then performed on-line using an Additive Factorial Hidden Markov Model from the generated appliance model parameters. Our solution is implemented on the cloud and tested with public benchmark datasets. Accuracy results are presented and compared with literature solutions, showing that the proposed solution achieves on-line detection with comparable detection performance with respect to non on-line approaches

Non-parametric modeling in non-intrusive load monitoring

Non-intrusive Load Monitoring (NILM) is an approach to the increasingly important task of residential energy analytics. Transparency of energy resources and consumption habits presents opportunities and benefits at all ends of the energy supply-chain, including the end-user. At present, there is no feasible infrastructure available to monitor individual appliances at a large scale. The goal of NILM is to provide appliance monitoring using only the available aggregate data, side-stepping the need for expensive and intrusive monitoring equipment. The present work showcases two self-contained, fully unsupervised NILM solutions: the first featuring non-parametric mixture models, and the second featuring non-parametric factorial Hidden Markov Models with explicit duration distributions. The present implementation makes use of traditional and novel constraints during inference, showing marked improvement in disaggregation accuracy with very little effect on computational cost, relative to the motivating work. To constitute a complete unsupervised solution, labels are applied to the inferred components using a Res-Net-based deep learning architecture. Although this preliminary approach to labelling proves less than satisfactory, it is well-founded and several opportunities for improvement are discussed. Both methods, along with the labelling network, make use of block-filtered data: a steady-state representation that removes transient behaviour and signal noise. A novel filter to achieve this steady-state representation that is both fast and reliable is developed and discussed at length. Finally, an approach to monitor the aggregate for novel events during deployment is developed under the framework of Bayesian surprise. The same non-parametric modelling can be leveraged to examine how the predictive and transitional distributions change given new windows of observations. This framework is also shown to have potential elsewhere, such as in regularizing models against over-fitting, which is an important problem in existing supervised NILM

Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree

open access articleProviding the user with appliance-level consumption data is the core of each energy efficiency system. To that end, non-intrusive load monitoring is employed for extracting appliance specific consumption data at a low cost without the need of installing separate submeters for each electrical device. In this context, we propose in this paper a novel non-intrusive appliance recognition system based on (i) detecting events in the aggregated power signal using a novel and powerful scheme, (ii) applying multiscale wavelet packet tree to collect comprehensive energy consumption features, and (iii) adopting an ensemble bagging tree classifier along with comparing its performance with various machine learning schemes. Moreover, to validate the proposed model, an empirical investigation is conducted on two real and public energy consumption datasets, namely, the GREEND and REDD, in which consumption readings are collected at low-frequencies. In addition, a comprehensive review of recent non-intrusive load monitoring approaches has been conducted and presented, in which their characteristics, performances and limitations are described. The proposed non-intrusive load monitoring system shows a high appliance recognition performance in terms of the accuracy, F1 score and low time complexity when it has been applied to different households from the GREEND and REDD repositories, in which every house includes various domestic appliances. Obtained results have described, e.g., that average accuracies of 97.01% and 96.36% have been reached on the GREEND and REDD datasets, respectively, which outperformed almost existing solutions considered in this framework

Thresholding methods in non-intrusive load monitoring

Non-intrusive load monitoring (NILM) is the problem of predicting the status or consumption of individual domestic appliances only from the knowledge of the aggregated power load. NILM is often formulated as a classifcation (ON/OFF) problem for each device. However, the training datasets gathered by smart meters do not contain these labels, but only the electric consumption at every time interval. This paper addresses a fundamental methodological problem in how a NILM problem is posed, namely how the diferent possible thresholding methods lead to diferent classifcation problems. Standard datasets and NILM deep learning models are used to illustrate how the choice of thresholding method afects the output results. Some criteria that should be considered for the choice of such methods are also proposed. Finally, we propose a slight modifcation to current deep learning models for multi-tasking, i.e. tackling the classifcation and regression problems simultaneously. Transfer learning between both problems might improve performance on each of them.Funding for open access publishing: Universidad de Cádiz/CBUA. This research has been financed in part by the Spanish Agencia Estatal de Investigación under grants PID2021-122154NB-I00 and TED2021-129455B-I00, and by a 2021 BBVA Foundation project for research in Mathematics. He also acknowledges support from the EU under the 2014-2020 ERDF Operational Programme and the Department of Economy, Knowledge, Business and University of the Regional Government of Andalusia (FEDER-UCA18-108393)

Designing Artificial Neural Networks (ANNs) for Electrical Appliance Classification in Smart Energy Distribution Systems

En este proyecto se abordará el problema de la desagregación del consumo eléctrico a través del diseño de sistemas inteligentes, basados en redes neuronales profundas, que puedan formar parte de sistemas más amplios de gestión y distribución de energía. Durante la definición estará presente la búsqueda de una complejidad computacional adecuada que permita una implementación posterior de bajo costo. En concreto, estos sistemas realizarán el proceso de clasificación a partir de los cambios en la corriente eléctrica provocados por los distintos electrodomésticos. Para la evaluación y comparación de las diferentes propuestas se hará uso de la base de datos BLUED.This project will address the energy consumption disaggregation problem through the design of intelligent systems, based on deep artificial neural networks, which would be part of broader energy management and distribution systems. The search for adequate computational complexity that will allow a subsequent implementation of low cost will be present during algorithm definition. Specifically, these systems will carry out the classification process based on the changes caused by the different appliances in the electric current. For the evaluation and comparison of the different proposals, the BLUED database will be used.Máster Universitario en Ingeniería Industrial (M141

Outlier Detection in Energy Datasets

In the past decade, numerous datasets have been released with the explicit goal of furthering non-intrusive load monitoring research (NILM). NILM is an energy measurement strategy that seeks to disaggregate building-scale loads. Disaggregation attempts to turn the energy consumption of a building into its constituent appliances. NILM algorithms require representative real-world measurements which has led institutions to publish and share their own datasets. NILM algorithms are designed, trained, and tested using the data presented in a small number of these NILM datasets. Many of the datasets contain arbitrarily selected devices. Likewise, the datasets themselves report aggregate load information from building(s) which are similarly selected arbitrarily. This raises the question of the representativeness of the datasets themselves as well as the algorithms based on their reports. One way to judge the representativeness of NILM datasets is to look for the presence of outliers in these datasets. This paper presents a novel method of identifying outlier devices from NILM datasets. With this identification process, it becomes possible to mitigate and measure the impact of outliers. This represents an important consideration to the long-term deployment of NILM algorithms

On a training-less solution for non-intrusive appliance load monitoring using graph signal processing

With ongoing large-scale smart energy metering deployments worldwide, disaggregation of a household’s total energy consumption down to individual appliances using analytical tools, aka. non-intrusive appliance load monitoring (NALM), has generated increased research interest lately. NALM can deepen energy feedback, support appliance retrofit advice and support home automation. However, despite the fact that NALM was proposed over 30 years ago, there are still many open challenges with respect to its practicality and effectiveness at low sampling rates. Indeed, the majority of NALM approaches, supervised or unsupervised, require training to build appliance models, and are sensitive to appliance changes in the house, thus requiring regular re-training. In this paper, we tackle this challenge by proposing a NALM approach that does not require any training. The main idea is to build upon the emerging field of graph signal processing to perform adaptive thresholding, signal clustering and pattern matching. We determine the performance limits of our approach and demonstrate its usefulness in practice. Using two open access datasets - the US REDD dataset with active power measurements downsampled to 1min resolution and the UK REFIT dataset with 8sec resolution, we demonstrate the effectiveness of the proposed method for typical smart meter sampling rate, with state-of-the-art supervised and unsupervised NALM approaches as benchmarks

Multi-timescale Event Detection in Nonintrusive Load Monitoring based on MDL Principle

Load event detection is the fundamental step for the event-based non-intrusive load monitoring (NILM). However, existing event detection methods with fixed parameters may fail in coping with the inherent multi-timescale characteristics of events and their event detection accuracy is easily affected by the load fluctuation. In this regard, this paper extends our previously designed two-stage event detection framework, and proposes a novel multi-timescale event detection method based on the principle of minimum description length (MDL). Following the completion of step-like event detection in the first stage, a long-transient event detection scheme with variable-length sliding window is designed for the second stage, which is intended to provide the observation and characterization of the same event at different time scales. In that, the context information in the aggregated load data is mined by motif discovery, and then based on the MDL principle, the proper observation scales are selected for different events and the corresponding detection results are determined. In the post-processing step, a load fluctuation location method based on voice activity detection (VAD) is proposed to identify and remove the unreasonable events caused by fluctuations. Based on newly proposed evaluation metrics, the comparison tests on public and private datasets demonstrate that our method achieves higher detection accuracy and integrity for events of various appliances across different scenarios.Comment: 11 pages,16 figure