Energy Disaggregation Using Elastic Matching Algorithms

Abstract

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.

Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors

“…At the preprocessing of the aggregated signal a median filter of five samples was used for smoothing as proposed in [59], and afterwards the preprocessed signal was segmented in overlapping frames of length equal to L = 10 samples and time shift between successive frames equal to 5 samples. The optimal number of samples per frame was determined through grid search on a bootstrap dataset with ideal aggregated data (without ghost power), consisting of one dataset out of each database (ECO-2, REDD-2 and iAWE) similar as in [67,68].…”

“…In order to directly compare the proposed methodology with other approaches proposed in the literature we additionally tested our method on five selected loads from the REDD-2 dataset, namely the refrigerator, lighting, dishwasher, microwave, and furnace. These loads were used in [55] because they carry a large percentage of the overall consumed energy and they have been used in other publications [67,75]. Furthermore, the disaggregation results were evaluated both in a noisy (with ghost data) and a noiseless (with synthetic data) setup as in [75] for both the one-stage and the proposed two-stage fusion architecture.…”

Multivariate Non-Negative Matrix Factorization with Application to Energy Disaggregation

“…First, machine learning has been used including Convolutional Neural Networks (CNNs) [3,4] or Long-Short-Term-Memory (LSTM) [5,6] as regression models for estimating the power consumption on device level as well as Hidden Markov Models (HMMs) [7,8]. Second, template matching techniques have been utilized for finding best matches between appliances and aggregated signatures using dynamic time warping [9] and elastic matching [10]. Third, source separation approaches have been used in order to separate the aggregated signal into its subcomponents, i.e.…”

Modelling the Disaggregated Demand for Electricity in Residential Buildings Using Artificial Neural Networks (Deep Learning Approach)

“…Data from Dataport had a lower sampling rate of 1/60 Hz or 1/3600 Hz. Schirmer et al [19] tested five different elastic matching algorithms in NILM based on REDD. The minimum variance matching (MVM) achieved the best results measured by both Accuracy and F 1 -Measure (definition in Section 2.6, Formula (6)) at 87.58% and 89.19% respectively.…”

“…The minimum variance matching (MVM) achieved the best results measured by both Accuracy and F 1 -Measure (definition in Section 2.6, Formula (6)) at 87.58% and 89.19% respectively. As noted by Schirmer et al [19], in contrast to the algorithms they used, approaches based on machine learning require a much larger dataset in order to train the model. De Paiva Penha and Castro [20] applied the convolutional neural network (CNN) approach to model the activity of six appliances in six houses using data from the REDD.…”