A time-frequency approach for event detection in non-intrusive load monitoring

Jin, Yufeng; Tebekaemi, Eniye; Bergés, Mario; Soibelman, Lúcio

doi:10.1117/12.884385

Cited by 30 publications

(14 citation statements)

References 16 publications

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“…True Positives (TP) is the number of successful detections, False Positives (FP) is the number of detections that do not correspond to actual events, while False Negatives (FN) is the number of missed events. ‫ܨ‬ ଵ − score = 2 × precision × recall precision + recall (10) where ‫ܧ‬ is the number of events. Results show highly precise detection rates where the number of false positives is relatively low in both phases.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Basseville and Nikiforov [6] described various detection algorithms from which two approaches have been utilized in event-based NILM systems, namely the Generalized Likelihood Ratio (GLR) test [7,8] and the CUmulative SUM (CUSUM) filtering [9]. Jin et al [10] proposed a more robust change-point detection approach based on a Goodness-of-Fit (GoF) test. In addition, various machine learning tools such as kernel clustering [11], Hidden Markov Models (HMM) [12], and Support Vector Machines (SVMs) [13], have been proposed as solutions to address the change point detection problem.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sequential Clustering-Based Event Detection for Non-Intrusive Load Monitoring

Barsim¹,

Yang²

2016

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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The problem of change-point detection has been well studied and adopted in many signal processing applications. In such applications, the informative segments of the signal are the stationary ones before and after the change-point. However, for some novel signal processing and machine learning applications such as Non-Intrusive Load Monitoring (NILM), the information contained in the non-stationary transient intervals is of equal or even more importance to the recognition process. In this paper, we introduce a novel clustering-based sequential detection of abrupt changes in an aggregate electricity consumption profile with accurate decomposition of the input signal into stationary and non-stationary segments. We also introduce various event models in the context of clustering analysis. The proposed algorithm is applied to building-level energy profiles with promising results for the residential BLUED power dataset.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sequential Clustering-Based Event Detection for Non-Intrusive Load Monitoring

Barsim¹,

Yang²

2016

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…Expert heuristics describe mostly rule-based approaches that consider prior knowledge to define sets of parameters and thresholds [8,17]. Probabilistic models consider statistical metrics, including variance and standard deviation, to estimate the probability of a change in a time series [11,22]. Approaches of the matched-filter category try to find a universal event pattern in the signal by exceeding a likelihood threshold [27,33].…”

Section: Lightmentioning

confidence: 99%

“…They do not allow to distinguish between different kinds of events or ignore undesired events. [7] 80.04 Jin et al [22] 81.01 Wild et al [35] 89.15…”

Section: Lightmentioning

confidence: 99%

Appliance Event Detection - A Multivariate, Supervised Classification Approach

Kahl

Kriechbaumer

Jorde

et al. 2019

Proceedings of the Tenth ACM International Conference on Future Energy Systems

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Non-intrusive load monitoring (NILM) is a modern and still expanding technique, helping to understand fundamental energy consumption patterns and appliance characteristics. Appliance event detection is an elementary step in the NILM pipeline. Unfortunately, several types of appliances (e.g., switching mode power supply (SMPS) or multi-state) are known to challenge state-of-theart event detection systems due to their noisy consumption profiles. Classical rule-based event detection system become infeasible and complex for these appliances. By stepping away from distinct event definitions, we can learn from a consumer-configured event model to differentiate between relevant and irrelevant event transients.We introduce a boosting oriented adaptive training, that uses false positives from the initial training area to reduce the number of false positives on the test area substantially. The results show a false positive decrease by more than a factor of eight on a dataset that has a strong focus on SMPS-driven appliances. To obtain a stable event detection system, we applied several experiments on different parameters to measure its performance. These experiments include the evaluation of six event features from the spectral and time domain, different types of feature space normalization to eliminate undesired feature weighting, the conventional and adaptive training, and two common classifiers with its optimal parameter settings. The evaluations are performed on two publicly available energy datasets with high sampling rates: BLUED and BLOND-50.

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“…There is a growing interest in real-time detection of any event occurrence in the power system. In [1], Jin et al introduce a joint time-frequency (TF) approach for event detection and change-point estimation in non-intrusive load monitoring. Transient disturbances are defined as short-lasting deviations of a measurement from its previous, i.e.…”

Section: Introductionmentioning

confidence: 99%