Non-Intrusive Electrical Appliances Monitoring and Classification using K-Nearest Neighbors (original) (raw)
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Nonintrusive energy disaggregation by detecting similarities in consumption patterns
Revista Facultad de Ingeniería, Universidad de Antioquia, 2020
Breaking down the aggregated energy consumption into a detailed consumption per appliance is a crucial tool for energy efficiency in residential buildings. Non-intrusive load monitoring allows implementing this strategy using just a smart energy meter without installing extra hardware. The obtained information is critical to provide an accurate characterization of energy consumption in order to avoid an overload of the electric system, and also to elaborate special tariffs to reduce the electricity cost for users. This article presents an approach for energy consumption disaggregation in households, based on detecting similar consumption patterns from previously recorded labelled datasets. The experimental evaluation of the proposed method is performed over four different problem instances that model real household scenarios using data from an energy consumption repository. Experimental results are compared with two built-in algorithms provided by the nilmtk framework (combinatorial optimization and factorial hidden Markov model). The proposed algorithm was able to achieve accurate results regarding standard prediction metrics. The accuracy was not affected in a significant manner by the presence of ambiguity between the energy consumption of different appliances or by the difference of consumption between training and test appliances.
Low-complexity low-rate residential non-intrusive appliance load monitoring
2017
Large-scale smart metering deployments and energy saving targets across the world have ignited renewed interest in residential non-intrusive appliance load monitoring (NALM), that is, disaggregating total household's energy consumption down to individual appliances, using purely analytical tools. Despite increased research efforts, NALM techniques that can disaggregate power loads at low sampling rates are still not accurate and/or practical enough, requiring substantial customer input and long training periods. In this thesis, we address these challenges via a practical lowcomplexity low-rate NALM, by proposing two approaches based on a combination of the following machine learning techniques: k-means clustering and Support Vector Machine, exploiting their strengths and addressing their individual weaknesses. The first proposed supervised approach is a low-complexity method that requires very short training period and is robust to labelling errors. The second, unsupervised approach relies on a database of appliance signatures that we designed using publicly available datasets. The database compactly represents over 100 appliances using statistical modelling of measured active power. Experimental results on three datasets from US (REDD), Italy and Austria (GREEND) and UK (REFIT), demonstrate the reliability and practicality of the proposed approaches.
Noninvasive Detection of Appliance Utilization Patterns in Residential Electricity Demand
Energies, 2021
Smart meters with automatic meter reading functionalities are becoming popular across the world As a result, load measurements at various sampling frequencies are now available Several methods have been proposed to infer device usage characteristics from household load measurements However, many techniques are based on highly intensive computations that incur heavy computational costs;moreover, they often rely on private household information In this paper, we propose a technique for the detection of appliance utilization patterns using low-computational-cost algorithms that do not require any information about households Appliance utilization patterns are identified only from the system status behavior, represented by large system status datasets, by using dimensionality reduction and clustering algorithms Principal component analysis, k-means, and the elbow method are used to define the clusters, and the minimum spanning tree is used to visualize the results that show the appearan...
Low-complexity energy disaggregation using appliance load modelling
AIMS Energy, 2016
Large-scale smart metering deployments and energy saving targets across the world have ignited renewed interest in residential non-intrusive appliance load monitoring (NALM), that is, disaggregating total household's energy consumption down to individual appliances, using purely analytical tools. Despite increased research efforts, NALM techniques that can disaggregate power loads at low sampling rates are still not accurate and/or practical enough, requiring substantial customer input and long training periods. In this paper, we address these challenges via a practical low-complexity lowrate NALM, by proposing two approaches based on a combination of the following machine learning techniques: k-means clustering and Support Vector Machine, exploiting their strengths and addressing their individual weaknesses. The first proposed supervised approach is a low-complexity method that requires very short training period and is fairly accurate even in the presence of labelling errors. The second approach relies on a database of appliance signatures that we designed using publicly available datasets. The database compactly represents over 200 appliances using statistical modelling of measured active power. Experimental results on three datasets from US, Italy, Austria and UK, demonstrate the reliability and practicality.
Transfer learning for non-intrusive load monitoring and appliance identification in a smart home
arXiv (Cornell University), 2023
Non-intrusive load monitoring (NILM) or energy disaggregation is an inverse problem whereby the goal is to extract the load profiles of individual appliances, given an aggregate load profile of the mains of a home. NILM could help identify the power usage patterns of individual appliances in a home, and thus, could help realize novel energy conservation schemes for smart homes. In this backdrop, this work proposes a novel deep-learning approach to solve the NILM problem and a few related problems as follows. 1) We build upon the reputed seq2-point convolutional neural network (CNN) model to come up with the proposed seq2-[3]-point CNN model to solve the (home) NILM problem and site-NILM problem (basically, NILM at a smaller scale). 2) We solve the related problem of appliance identification by building upon the state-of-the-art (pretrained) 2D-CNN models, i.e., AlexNet, ResNet-18, and DenseNet-121, which are trained upon two custom datasets that consist of Wavelets and short-time Fourier transform (STFT)-based 2D electrical signatures of the appliances. 3) Finally, we do some basic qualitative inference about an individual appliance's health by comparing the power consumption of the same appliance across multiple homes. Low-frequency REDD dataset is used to train and test the proposed deep learning models for all problems, except site-NILM where REFIT dataset has been used. As for the results, we achieve a maximum accuracy of 94.6% for home-NILM, 81% for site-NILM, and 88.9% for appliance identification (with Resnet-based model).
Non-intrusive load monitoring (NILM) is a low-cost alternative to appliance level sub-metering, that leverages signal processing and machine learning techniques to estimate the power consumption of individual appliances from whole-home measurements. However, the difficulty associated with obtaining training data sets for the commonly used supervised NILM classification algorithms is a major obstacle in wide commercial adoption of the technology. The diversity of electrical load signatures (patterns of appliances' power draw) demands in-situ training (labeling of the signatures), which often needs to be performed by ordinary users through usersystem interaction. To produce the example signatures required for training, continuous interaction with users might be required, which could reduce the success of the training process due to user fatigue. Pre-populating the training data set could help facilitate the process by reducing the number of user-system interactions needed for labeling. Taking into consideration all the issues described above, a study to test the feasibility of autonomous clustering of similar appliances' signatures based on hierarchical clustering was investigated. The information contained in the structure of the binary cluster tree was used for clustering without the need for a priori selection of the number of clusters. The assessment, carried out on data collected from a residential setting, showed promising results (with accuracy above 90%, calculated based on the ground truth labels) supporting the feasibility of the approach for unsupervised clustering.
ANN-based appliance recognition from low-frequency energy monitoring data
2013 IEEE 14th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM), 2013
The rational use and management of energy is a key objective for the evolution towards the smart grid. In particular in the private home domain the adoption of widescale energy consumption monitoring techniques can help end users in optimizing energy consumption behaviors. While most existing approaches for load disaggregation and classification requires high-frequency monitoring data, in this paper we propose an approach for detecting and identifying the appliances in use by analysing low-frequency monitoring data gathered by meters (i.e. smart plugs) distributed in the home. Our approach implements a supervised classification algorithm with artificial neural networks and has been tested with a dataset of power traces collected in real-world home settings.
Non-Intrusive Appliance Identification with Appliance-Specific Networks
IEEE Transactions on Industry Applications , 2020
The problem of Non-Instrusive Load Monitoring (NILM) is usually formulated as a single-channel blind source separation task, whose successful solution enable fast and convenient load identification and energy disaggregation. When applied at test time, NILM algorithms aim to identify the operating characteristics of individual appliances from an aggregate power measurement of the entire house. Recent advances in deep learning gave rise to many methods that mostly focus on learning a direct mapping from aggregate measurement to individual appliance power. However, these methods are not only computationally expensive, but they often suffer from overfitting and don't generalize very well. In this paper, we propose a novel NILM method that leverages advances in statistical learning that haven't been properly applied in this domain before. The proposed method consists of three stages: a) a Bayesian non-parametric learning-based approach for appliance state extraction; b) synthetic minority oversampling technique (SMOTE) for data augmentation and mitigating the heavy imbalance in switching events; and c) appliance-specific lightweight long short-term memory (LSTM) networks for status classification for each appliance. We adopt a “differential” input (the difference before and after the switching event) to reduce the complexity of network training and make the proposed method robust to multi-appliance switching events. Experiments are conducted to demonstrate the effectiveness of the proposed method, achieving superior performance when compared to recent methods. An ablation study is conducted to demonstrate the effectiveness of each module of our method. Finally, we investigate the quality of generated synthetic samples.
Non-intrusive load monitoring using multi-label classification methods
Electrical Engineering, 2020
Non-intrusive load monitoring is a technique to help power companies monitor and analyze residential energy usage. Aggregated power load measurements for a household (i.e., the signal on the main powerline) are disaggregated into individual appliance loads by examining the appliance-specific power consumption characteristics. These data can then be used to modify consumer behaviors via detailed billing and/or demand-pricing tariffs. A number of advances in the field have been reported in the past two decades, many of which apply machine learning algorithms. However, these algorithms usually only assign one label to an example, which is a poor match to the monitoring problem, meaning elaborate encodings or classifier ensembles are needed. A more elegant solution would be to use algorithms that assign multiple labels to a single example. These multi-label classification algorithms have received very little attention in this field to date. We conduct an experimental investigation of four multi-label classification algorithms for non-intrusive monitoring and find that the best one is superior to the existing reported results on multiple real-world household datasets. Keywords Non-intrusive load monitoring • Load disaggregation • Multi-label classification List of symbols L The number of household appliances χ t The aggregated power load at time instant t N (x) The set of k-nearest neighbors of x − → y x
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