Generalized algorithm for the non-intrusive identification of electrical appliances in the household

Bilski, Piotr; Winiecki, W.

doi:10.1109/idaacs.2017.8095186

Cited by 11 publications

(19 citation statements)

References 8 publications

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“…The NILM approaches can briefly be classified into methods with and without source separation (SS). Approaches without SS are based on the decomposition of the aggregated signal to a sequence of feature vectors, which will be classified to device labels by a machine learning (ML) algorithm (e.g., artificial neural networks (ANN) [27], cecision trees (DT) [28], hidden Markov models (HMM) [22], k-nearest neighbors (KNN) [29], support vector machines (SVM) [30]) or by a predefined set of rules and thresholds [31,32]. Furthermore, recent research in deep learning and big data has led to a significant increase of use of data-driven approaches using large scale datasets (e.g., AMPds [33]).…”

Section: Introductionmentioning

confidence: 99%

Energy Disaggregation Using Elastic Matching Algorithms

Schirmer

Mporas

Paraskevas

2020

Entropy

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In this article an energy disaggregation architecture using elastic matching algorithms is presented. The architecture uses a database of reference energy consumption signatures and compares them with incoming energy consumption frames using template matching. In contrast to machine learning-based approaches which require significant amount of data to train a model, elastic matching-based approaches do not have a model training process but perform recognition using template matching. Five different elastic matching algorithms were evaluated across different datasets and the experimental results showed that the minimum variance matching algorithm outperforms all other evaluated matching algorithms. The best performing minimum variance matching algorithm improved the energy disaggregation accuracy by 2.7% when compared to the baseline dynamic time warping algorithm.

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Section: Introductionmentioning

confidence: 99%

Energy Disaggregation Using Elastic Matching Algorithms

Schirmer

Mporas

Paraskevas

2020

Entropy

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“…In particular, there is a lack of experimental results in which the accuracy of particular appliances was evaluated. In our research, a multilateral approach [56] analyzing data obtained using various measurement setups was applied. We prepared a comprehensive NIALM laboratory adapted to perform various experiments.…”

Section: Discussionmentioning

confidence: 99%

Nonintrusive Appliance Load Monitoring: An Overview, Laboratory Test Results and Research Directions

Wójcik

Łukaszewski

Kowalik

et al. 2019

Sensors

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Nonintrusive appliance load monitoring (NIALM) allows disaggregation of total electricity consumption into particular appliances in domestic or industrial environments. NIALM systems operation is based on processing of electrical signals acquired at one point of a monitored area. The main objective of this paper was to present the state-of-the-art in NIALM technologies for the smart home. This paper focuses on sensors and measurement methods. Different intelligent algorithms for processing signals have been presented. Identification accuracy for an actual set of appliances has been compared. This article depicts the architecture of a unique NIALM laboratory, presented in detail. Results of developed NIALM methods exploiting different measurement data are discussed and compared to known methods. New directions of NIALM research are proposed.

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“…NILM methods may use macroscopic signal parameters (e.g., active/reactive power [24,25]) or microscopic ones (e.g., transient energy and harmonics [26][27][28]), depending on the sampling rate f s , to split the aggregated signal in appliance level [29]. Appliance identification methods not using SS algorithms are based mainly on supervised methods and the extraction of features, which will be used either for training a Machine Learning (ML) algorithm (e.g., Support Vector Machines (SVM) [30], Artificial Neural Network (ANN) [31], Decision Tree (DT) [32], K-Nearest Neighbours (KNN) [33]), or defining a set of rules or thresholds [28]. As regards appliance identification methods using SS algorithms, they are based on single-channel source separation and solve the task with optimization criteria.…”

Section: Introductionmentioning

confidence: 99%

Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors

2020

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A data-driven methodology to improve the energy disaggregation accuracy during Non-Intrusive Load Monitoring is proposed. In detail, the method uses a two-stage classification scheme, with the first stage consisting of classification models processing the aggregated signal in parallel and each of them producing a binary device detection score, and the second stage consisting of fusion regression models for estimating the power consumption for each of the electrical appliances. The accuracy of the proposed approach was tested on three datasets—ECO (Electricity Consumption & Occupancy), REDD (Reference Energy Disaggregation Data Set), and iAWE (Indian Dataset for Ambient Water and Energy)—which are available online, using four different classifiers. The presented approach improves the estimation accuracy by up to 4.1% with respect to a basic energy disaggregation architecture, while the improvement on device level was up to 10.1%. Analysis on device level showed significant improvement of power consumption estimation accuracy especially for continuous and nonlinear appliances across all evaluated datasets.

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Generalized algorithm for the non-intrusive identification of electrical appliances in the household

Cited by 11 publications

References 8 publications

Energy Disaggregation Using Elastic Matching Algorithms

Energy Disaggregation Using Elastic Matching Algorithms

Nonintrusive Appliance Load Monitoring: An Overview, Laboratory Test Results and Research Directions

Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors

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