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.
Highlights
In recent years, the world energy demand has increased due to the population growth and economic development [1] and it is expected that it will further increase in the decades [2].The energy demand worldwide is annually increasing both in the residential and the industrial sector with households consuming approximately 40% of the world’s consumed energy [3,4].The technological development of the last decades has led to low costs for buying electrical appliances and the automation of tasks and procedures both in industry and in households, it is estimated that the electric power needs will further grow and the average number of electrical appliances per household will significantly increase within the two decades [4]
The performance was evaluated in terms of estimation accuracy (E ACC ) considering device operation in state level with a double counting of errors as proposed in [55], i.e., E ACC = 1 −
A 10-fold cross validation protocol was followed, with 90% of the data being used for building the signature database and 10% of the data for evaluating the proposed elastic matching-based non-intrusive load monitoring (NILM) architecture
Summary
The world energy demand has increased due to the population growth and economic development [1] and it is expected that it will further increase in the decades [2]. In the NILM approach the energy disaggregation task is expressed as a single-channel source separation problem, where the smart meter is the only input channel measuring the total power consumption and the goal is to find the inverse of the aggregation function to calculate the energy consumption per device. In contrast to one/multi-state devices, there is no established approach in detecting appliances with continuous power consumption or with non-linear behavior and a highly-varying power signature [19,20] Researchers have addressed this issue by using high frequency features or wavelets to detect transient device behavior, these have the drawback of a higher cost in hardware and an increased computational power needed [12,20,21].
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