A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring

Cominola, Andrea; Giuliani, Matteo; Piga, Dario; Castelletti, Andrea; Rizzoli, Andrea Emilio

doi:10.1016/j.apenergy.2016.10.040

Cited by 131 publications

(86 citation statements)

References 34 publications

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“…After the disaggregation process, single-use events could be categorized by clustering analysis or any other classification algorithm [15]. Similar methodologies have already been used in other fields, like non-intrusive electric load data disaggregation [26][27][28][29]. As an example of the results that can be achieved by these techniques, Figure 10 shows the findings for one of the most complex households, HH-06 of R1, showing an indoor leak and a high average daily consumption.…”

Section: Resultsmentioning

confidence: 99%

Water End Use Disaggregation Based on Soft Computing Techniques

Pastor-Jabaloyes

Arregui

Cobacho

2018

Water

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Disaggregating residential water end use events through the available commercial tools needs a great investment in time to manually process smart metering data. Therefore, it is extremely difficult to achieve a homogenous and sufficiently large corpus of classified single-use events capable of accurately describe residential water consumption. The main goal of the present paper is to develop an automatic tool that facilitates the disaggregation of the individual water consumptions events from the raw flow trace. The proposed disaggregation methodology is conducted through two actions that are iteratively performed: first, the use of an advanced two-step filter, whose calibration is automatically conducted by the Elitist Non-Dominated Sorting Genetic Algorithm NSGA-II; and second, a cropping algorithm based on the filtered water consumption flow traces. As a secondary goal, yet complementary to the main one, a semiautomatic massive classification process has been developed, so that the resulting single-use events can be easily categorized in the different water end uses in a household. This methodology was tested using water consumption data from two different case studies. The characteristics of the households taken as reference and their occupants were unequivocally dissimilar from each other. In addition, the monitoring equipment used to obtain the consumption flow traces had completely different technical specifications. The results obtained from the processing of the two studies show that the automatic disaggregation is both robust and accurate, and produces significant time saving compared to the standard manual analysis.

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Section: Resultsmentioning

confidence: 99%

Water End Use Disaggregation Based on Soft Computing Techniques

Pastor-Jabaloyes

Arregui

Cobacho

2018

Water

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“…As a baseline NILM approach, we consider a data-driven energy disaggregation methodology without the use of SS techniques, adopted in several publications found in the literature [39,[43][44][45][46]. The baseline NILM consists of preprocessing of the aggregated signal P agg , then decomposition of the sequence of frames to a sequence of feature vectors followed by processing from a classification/regression algorithm using pre-trained appliances' models to determine device operation as shown in Fig.…”

Section: Baseline Nilm Architecturementioning

confidence: 99%

Robust energy disaggregation using appliance-specific temporal contextual information

Schirmer

Mporas

Sheikh-Akbari

2020

EURASIP J. Adv. Signal Process.

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An extension of the baseline non-intrusive load monitoring approach for energy disaggregation using temporal contextual information is presented in this paper. In detail, the proposed approach uses a two-stage disaggregation methodology with appliance-specific temporal contextual information in order to capture time-varying power consumption patterns in low-frequency datasets. The proposed methodology was evaluated using datasets of different sampling frequency, number and type of appliances. When employing appliance-specific temporal contextual information, an improvement of 1.5% up to 7.3% was observed. With the two-stage disaggregation architecture and using appliance-specific temporal contextual information, the overall energy disaggregation accuracy was further improved across all evaluated datasets with the maximum observed improvement, in terms of absolute increase of accuracy, being equal to 6.8%, thus resulting in a maximum total energy disaggregation accuracy improvement equal to 10.0%.

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“…Perhaps, the biggest challenge of NILM is not technology but privacy [10,17]. Scalability is to be allowed while minimizing the expenses of sensor installation, data collection/analysis, and privacy protection [30]. This study considered privacy issues from two perspectives.…”

Section: Contribution and Limitationmentioning

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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A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring

Cited by 131 publications

References 34 publications

Water End Use Disaggregation Based on Soft Computing Techniques

Water End Use Disaggregation Based on Soft Computing Techniques

Robust energy disaggregation using appliance-specific temporal contextual information

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

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