Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors

Himeur, Yassine; Alsalemi, Abdullah; Bensaali, Fayçal; Amira, Abbes

doi:10.1002/int.22404

Cited by 78 publications

(38 citation statements)

References 67 publications

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“…There are several methods to identify the most suitable installation spot, one of which is based on intuition and F I G U R E 3 Illustration of the hardware implementation: (A) the Sonoff POW smart plug, and (B) the (EM) 3 hardware setup installed in QUEL. 31 | 7135 experience. One of the core aspects is sensor placement.…”

Section: Sensors/devices For Data Collectionmentioning

confidence: 99%

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Himeur

Alsalemi

Bensaali

et al. 2022

Int J of Intelligent Sys

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Smart nonintrusive load monitoring (NILM) represents a cost-efficient technology for observing power usage in buildings. It tackles several challenges in transitioning into a more effective, sustainable, and digital energy efficiency environment. This paper presents a comprehensive review of recent trends in the NILM field, in which we propose a multiperspective classification of existing smart NILM techniques. More attention is devoted to describing the contributions of deep learning, feature extraction, computing platforms, and application scenarios for NILM development. Accordingly, NILM technical aspects are first Int J Intell Syst. 2022;37:7124-7179. wileyonlinelibrary.com/journal/int investigated, including data collection devices and public data sets. Next, event-based and non-eventbased NILM algorithms are overviewed. Furthermore, potential limitations of existing solutions are identified, highlighting their technical challenges, especially those related to security and privacy preservation, data scarcity, results reproduction, and implementation and business difficulties. Lastly, future directions are explored to overcome the identified limitations.

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Section: Sensors/devices For Data Collectionmentioning

confidence: 99%

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Himeur

Alsalemi

Bensaali

et al. 2022

Int J of Intelligent Sys

Self Cite

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“…Further, many other studies focus on machine learning approaches for abnormality detection in different application areas such as energy consumption 32 . Some of these approaches devised to detect energy consumption anomalies based on extracting micro‐moment features use a rule‐based model 33 and deep neural networks 34 …”

Section: Related Workmentioning

confidence: 99%

An efficient approach for detecting anomalous events in real‐time weather datasets

Arora

Rani

Saxena

2021

Concurrency and Computation

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Event detection in real‐time is applied in diverse domains such as detection of fraudulent activities in commercial transactions, detection of faulty systems in industries, and so forth. Businesses and organizations benefit from the actionable information obtained through various techniques available for anomalous event detection. Real‐time event detection is nowadays handled through streaming data frameworks. Traditional approaches effectively handle event detection in real‐time but with more false positives, thus, resulting in false alarms. In this article, an efficient approach comprising two components, an offline model and an online event detection pipeline, is proposed to achieve minimum mean absolute error (MAE). An offline module is developed to investigate a variety of deep learning models that prove suitable for event detection in real‐time. The experiments conducted with PubNub sensors datasets demonstrate that the long short‐term memory unit of recurrent neural networks is the best suitable model for anomalous event detection. The online pipeline module is built using streaming data frameworks to predict the abnormal peaks. It is revealed through the experimental results that the proposed approach efficiently detects anomalous events in real‐time and also eliminates false positives.

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“…This could figure out end-users' preferences and related information as well [229]. Consequently, by using NILM instead of submetering, the development cost of recommender systems will significantly be reduced [118,230].…”

Section: Non-intrusive Recommender Systemsmentioning

confidence: 99%

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Himeur¹,

Alsalemi²,

Al‐Kababji³

et al. 2021

Preprint

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Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems' performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors' knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.

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Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors

Cited by 78 publications

References 67 publications

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

An efficient approach for detecting anomalous events in real‐time weather datasets

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

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