Event Matching Classification Method for Non-Intrusive Load Monitoring

Azizi, Elnaz; Beheshti, Mohammad Taghi Hamidi; Bolouki, Sadegh

doi:10.3390/su13020693

Cited by 20 publications

(10 citation statements)

References 32 publications

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“…When the aggregated load curve is available at a high time resolution, many approaches are possible such as event matching, where device activation edges are identified and matched to appliances [8]. Deep learning has also been used to successfully disaggregate load curves using training data [9,10,11,12].…”

Section: Related Workmentioning

confidence: 99%

Domain Knowledge Aids in Signal Disaggregation; the Example of the Cumulative Water Heater

Belikov,

Matheron,

Sassi

2022

Preprint

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In this article we present an unsupervised low-frequency method aimed at detecting and disaggregating the power used by Cumulative Water Heaters (CWH) in residential homes. Our model circumvents the inherent difficulty of unsupervised signal disaggregation by using both the shape of a power spike and its time of occurrence to identify the contribution of CWH reliably. Indeed, many CHWs in France are configured to turn on automatically during off-peak hours only, and we are able to use this domain knowledge to aid peak identification despite the low sampling frequency.In order to test our model, we equipped a home with sensors to record the ground-truth consumption of a water heater. We then apply the model to a larger dataset of energy consumption of Hello Watt users consisting of one month of consumption data for 5k homes at 30-minute resolution. In this dataset we successfully identified CWHs in the majority of cases where consumers declared using them. The remaining part is likely due to possible misconfiguration of CWHs, since triggering them during off-peak hours requires specific wiring in the electrical panel of the house. Our model, despite its simplicity, offers promising applications: detection of mis-configured CWHs on off-peak contracts and slow performance degradation.

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Section: Related Workmentioning

confidence: 99%

Domain Knowledge Aids in Signal Disaggregation; the Example of the Cumulative Water Heater

Belikov,

Matheron,

Sassi

2022

Preprint

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“…Probably this is due to the fact that using additional features that do not add significant information only affect data sparsity, thus reducing the performance of a distance-based classification algorithm. A similar approach has been developed by Azizi et al (2021). In their work, an improved version of hierarchical clustering with Ward linkage, combined with the elbow method is applied.…”

Section: Related Workmentioning

confidence: 99%

Non-intrusive load monitoring techniques for the disaggregation of ON/OFF appliances

et al. 2022

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Nowadays, Non-Intrusive Load Monitoring techniques are sufficiently accurate to provide valuable insights to the end-users and improve their electricity behaviours. Indeed, previous works show that commonly used appliances (fridge, dishwasher, washing machine) can be easily disaggregated thanks to their abundance of electrical features. Nevertheless, there are still many ON/OFF devices (e.g. heaters, kettles, air conditioners, hair dryers) that present very poor power signatures, preventing their disaggregation with traditional algorithms. In this work, we propose a new online clustering method exploiting both operational features (peak power, duration) and external features (time of use, day of week, weekday/weekend) in order to recognize ON/OFF devices. The proposed algorithm is intended to support an existing disaggregation algorithm that is already able to classify at least 80% of the total energy consumption of the house. Thanks to our approach, we improved the performance of our existing disaggreation algorithm from 80% to 87% of the total energy consumption in the monitored houses. In particular, we found that 85% of the clusters were identified by only using operational features, while external features allowed us to identify the remaining 15% of the clusters. The algorithm needs to collect on average less than 40 operations to find a cluster, which demonstrates its applicability in the real world.

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“…Another prevalent definition focuses on the earliest start time and ending time of shiftable devices. Traditional EF characterization involved installing smart meters on residential devices and continuously monitoring data, which, although straightforward, could be costly and slow it also raised concerns about data privacy [8].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring

Shabbir,

Vassiljeva,

Nourollahi Hokmabad

et al. 2024

Electronics

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Non-intrusive load monitoring (NILM) has emerged as a pivotal technology in energy management applications by enabling precise monitoring of individual appliance energy consumption without the requirements of intrusive sensors or smart meters. In this technique, the load disaggregation for the individual device is accrued by the recognition of their current signals by employing machine learning (ML) methods. This research paper conducts a comprehensive comparative analysis of various ML techniques applied to NILM, aiming to identify the most effective methodologies for accurate load disaggregation. The study employs a diverse dataset comprising high-resolution electricity consumption data collected from an Estonian household. The ML algorithms, including deep neural networks based on long short-term memory networks (LSTM), extreme gradient boost (XgBoost), logistic regression (LR), and dynamic time warping with K-nearest neighbor (DTW-KNN) are implemented and evaluated for their performance in load disaggregation. Key evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of each technique in capturing the nuanced energy consumption patterns of diverse appliances. Results indicate that the XgBoost-based model demonstrates superior performance in accurately identifying and disaggregating individual loads from aggregated energy consumption data. Insights derived from this research contribute to the optimization of NILM techniques for real-world applications, facilitating enhanced energy efficiency and informed decision-making in smart grid environments.

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Event Matching Classification Method for Non-Intrusive Load Monitoring

Cited by 20 publications

References 32 publications

Domain Knowledge Aids in Signal Disaggregation; the Example of the Cumulative Water Heater

Domain Knowledge Aids in Signal Disaggregation; the Example of the Cumulative Water Heater

Non-intrusive load monitoring techniques for the disaggregation of ON/OFF appliances

Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring

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