Dynamic time warping based non-intrusive load transient identification

Liu, Bo; Luan, Wenpeng; Yu, Yixin

doi:10.1016/j.apenergy.2017.03.010

Cited by 104 publications

(48 citation statements)

References 35 publications

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“…Compared with intrusive appliance load monitoring (IALM) used in [15,21], NIALM does not require any extra investment, making it more and more popular. Currently there are many NIALM methods [23][24][25][26][27][28][29]. Most of NIALM methods disaggregate the energy consumption of different appliances mainly based on events detection, such as steady-state events [27][28][29][30] and transient-state events [23,26,31] related to current or voltage.…”

Section: Related Workmentioning

confidence: 99%

“…Currently there are many NIALM methods [23][24][25][26][27][28][29]. Most of NIALM methods disaggregate the energy consumption of different appliances mainly based on events detection, such as steady-state events [27][28][29][30] and transient-state events [23,26,31] related to current or voltage. Meanwhile, supervised [23] methods and unsupervised [27,28] methods are used to identify the energy consumption of appliances.…”

Section: Related Workmentioning

confidence: 99%

“…Actually, when using transient-state features, NIALM methods usually require very high sampling rates (e.g. greater than 1MHZ) [23,26,31], and when using steady-state features, NIALM methods usually require sampling rates greater than 1HZ [27][28][29][30]. Perez et al [30] has found that the accuracy of the NIALM methods decrease dramatically following with the increase of the sampling period.…”

Section: Related Workmentioning

confidence: 99%

“…Perez et al [30] has found that the accuracy of the NIALM methods decrease dramatically following with the increase of the sampling period. However, in this work, hourly aggregated energy consumption data is used, and the sampling rate is too low to capture the state changes of appliances, therefore NIALM methods [27][28][29][30] can not be used for this paper. There are a few NIALM methods using very low sampling rate data.…”

Section: Related Workmentioning

confidence: 99%

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“…The k-NN algorithm with any distance-based time series metrics was found to perform better than HMM. Liu et al [18] applied DTW to a field test. The mean F-measure values for identification were analyzed to be 92.82% and 88.15% for residential and commercial offices, respectively.…”

Section: Algorithmsmentioning

confidence: 99%

Load Disaggregation via Pattern Recognition: A Feasibility Study of a Novel Method in Residential Building

2018

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Abstract:In response to the need to improve energy-saving processes in older buildings, especially residential ones, this paper describes the potential of a novel method of disaggregating loads in light of the load patterns of household appliances determined in residential buildings. Experiments were designed to be applicable to general residential buildings and four types of commonly used appliances were selected to verify the method. The method assumes that loads are disaggregated and measured by a single primary meter. Following the metering of household appliances and an analysis of the usage patterns of each type, values of electric current were entered into a Hidden Markov Model (HMM) to formulate predictions. Thereafter, the HMM repeatedly performed to output the predicted data close to the measured data, while errors between predicted and the measured data were evaluated to determine whether they met tolerance. When the method was examined for 4 days, matching rates in accordance with the load disaggregation outcomes of the household appliances (i.e., laptop, refrigerator, TV, and microwave) were 0.994, 0.992, 0.982, and 0.988, respectively. The proposed method can provide insights into how and where within such buildings energy is consumed. As a result, effective and systematic energy saving measures can be derived even in buildings in which monitoring sensors and measurement equipment are not installed.

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Classifier Economics of Semi‐intrusive Load Monitoring

2022

Smart Energy for Transportation and Health in a Smart City

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Dynamic time warping based non-intrusive load transient identification

Cited by 104 publications

References 35 publications

Energy consumption analysis of residential swimming pools for peak load shaving

Energy consumption analysis of residential swimming pools for peak load shaving

Load Disaggregation via Pattern Recognition: A Feasibility Study of a Novel Method in Residential Building

Classifier Economics of Semi‐intrusive Load Monitoring

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