Exploiting Appliance State Constraints to Improve Appliance State Detection

Khandelwal, Tarun; Rajwanshi, Karan; Bharadwaj, Priti; Garani, Shayan Srinivasa; Sundaresan, Rajesh

doi:10.1145/3077839.3077859

Cited by 8 publications

(7 citation statements)

References 7 publications

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“…Literature reports few contributions for NILM algorithms applied to BLUED data at the same frequency of 1 Hz [13], [14], [18].…”

Section: Performance With Blued Datasetmentioning

confidence: 99%

“…In [14], clustering in ΔP -ΔQ plane is solved through a hierarchical approach executed with some manual supervision. The paper reports for phase-A BLUED data an F1-score for the event detection and for the appliance classification of about 92% and 88% respectively.…”

Section: Performance With Blued Datasetmentioning

confidence: 99%

“…However, few contributions have been proposed with low frequency samples. Among them, [13] and [14] will be considered in this work as comparison.…”

Section: Introductionmentioning

confidence: 99%

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NILM techniques applied to a real-time monitoring system of the electricity consumption

et al. 2021

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Non-Intrusive Load Monitoring (NILM) allows providing appliance-level electricity consumption information and decomposing the overall power consumption by using simple hardware (one sensor) with a suitable software. This paper presents a low-frequency NILM-based monitoring system suitable for a typical house. The proposed solution is a hybrid event-detection approach including an event-detection algorithm for devices with a finite number of states and an auxiliary algorithm for appliances characterized by complex patterns. The system was developed using data collected at households in Italy and tested also with data from BLUED, a widely used dataset of real-world power consumption data. Results show that the proposed approach works well in detecting and classifying what appliance is working and its consumption in complex household load dataset.

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“…Literature reports few contributions for NILM algorithms applied to BLUED data at the same frequency of 1 Hz [13], [14], [18].…”

Section: Performance With Blued Datasetmentioning

confidence: 99%

Section: Performance With Blued Datasetmentioning

confidence: 99%

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NILM techniques applied to a real-time monitoring system of the electricity consumption

et al. 2021

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“…Event-based algorithms have a relatively fixed processing procedure, including event detection, feature extraction and event classification. To obtain accurate identification results, different classification techniques are tried, including k-means [27], k-nearest neighbor (k-NN) [28], naïve Bayes [29], maximum likelihood [30] and decision tree (DT) [31]. In [30] the maximum likelihood classifier is designed to disaggregate load based on the power profiles, but it only works for single-state loads.…”

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration

Gao

et al. 2019

Applied Sciences

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Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to disaggregate the whole loads in a household, which leads to low identification accuracy. In this method, the event detection method is used to obtain the switching event sets of all loads, and the power consumption curves of independent unknown electrical appliances in a period are disaggregated by utilizing comprehensive features. A linear discriminant classifier group based on multi-feature global similarity is used for load identification. The uniqueness of our algorithm is that it designs an event detector based on steady-state segmentation and a linear discriminant classifier group based on multi-feature global similarity. The simulation is carried out on an open source data set. The results demonstrate the effectiveness and high accuracy of the multi-feature integrated classification (MFIC) algorithm by using the state-of-the-art NILM methods as benchmarks.

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“…Event-based algorithms have a relatively fixed processing procedure, including event detection, feature extraction and event classification. To obtain accurate identification results, different classification techniques are tried, including k-means [25], k-Nearest neighbour (k-NN) [26], naïve Bayes [27], maximum likelihood [28] and decision tree (DT) [29]. In [28] the maximum likelihood classifier is designed to disaggregate load based on the power profiles, but it only works for single-state loads.…”

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration

Yu¹,

Wu²,

Su³

2019

Preprint

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Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to decompose the whole loads in a household, which leads to low identification accuracy. In this paper, an NILM approach based on multi-feature integrated classification (MFIC) is explored, which combines some non-electrical features such as ON/OFF duration, usage frequency of appliances, and usage period to improve load differentiability. The implementation of MFIC algorithm is consistent with traditional event-based method. The uniqueness of our algorithm is that it designs an event detector based on steady-state segmentation and a linear discriminant classifier group based on multi-feature global similarity. Simulation results using an open-access dataset demonstrate the effectiveness and high accuracy of MFIC algorithm, with the state-of-the-art NILM methods as benchmarks.

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Exploiting Appliance State Constraints to Improve Appliance State Detection

Cited by 8 publications

References 7 publications

NILM techniques applied to a real-time monitoring system of the electricity consumption

NILM techniques applied to a real-time monitoring system of the electricity consumption

Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration

Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration

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