On the Applicability of 2D Local Binary Patterns for Identifying Electrical Appliances in Non-intrusive Load Monitoring

Himeur, Yassine; Alsalemi, Abdullah; Bensaali, Fayçal; Amira, Abbes; Sardianos, Christos; Varlamis, Iraklis; Dimitrakopoulos, George

doi:10.1007/978-3-030-55190-2_15

Cited by 15 publications

(9 citation statements)

References 32 publications

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“…But if we are only given Y , we should get the sampled query images from database images. And then we adopt the optimise problem in (5) for training the integral ADSH model. It ensures that the binary hash codes of sampled query images satisfy both the learned hash function and the result of direct learning.…”

Section: Loss Function Partmentioning

confidence: 99%

“…And then the maximum number of cycles is set to be 20. For the ADSH method, the hyper-parameter 𝛾 is set to be 200 by using a FIGURE 5 Total apparent power during some events FIGURE 6 The results of extracted V-I trajectories validation strategy. In addition, we set the mini-batch size to be 64 and tune the learning rate between [10 −6 , 10 −2 ].…”

Section: Experiments With Different Cnn Modelsmentioning

confidence: 99%

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Non‐intrusive load monitoring by voltage–current trajectory enabled asymmetric deep supervised hashing

Han

Huo

et al. 2021

IET Generation Trans & Dist

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With the development of smart grids, appliance-level data information plays a vital role in smart power consumption. Nowadays, appliance signatures detected by non-intrusive load monitoring (NILM) can be used for anomaly detection, demand response, and electricity management. These applications increase the requirements for the accuracy of appliance identification in NILM. And it has been proved that the voltage-current (V-I) trajectory can be applied as an effective load signature to represent the electrical characteristics of appliances with different statuses in previous researches. In this paper, a V-I trajectory enabled asymmetric deep supervised hashing (ADSH) method has been proposed for NILM. ADSH method converts load identification into the large-scale approximate nearest neighbour search. Different from the existing methods, ADSH treats the query images and database images in an asymmetric way in order to improve accuracy. More specifically, ADSH learns a deep hash function only for query images, while the hash codes for database images are directly learned. The experimental results on REDD and PLAID datasets show that the proposed method significantly improves the accuracy of load identification compared with state-of-art methods.

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Section: Loss Function Partmentioning

confidence: 99%

Section: Experiments With Different Cnn Modelsmentioning

confidence: 99%

Non‐intrusive load monitoring by voltage–current trajectory enabled asymmetric deep supervised hashing

Han

Huo

et al. 2021

IET Generation Trans & Dist

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“…A large variety of 2D descriptors have been proposed and successfully applied in various research topics. The energy efficiency community can benefit from those experiences to build powerful NILM systems [284]. In this context, the data fusion of 2D descriptors plays a major role in developing such solutions that not only improves the appliance identification at almost no cost, but also has a positive impact on promoting energy efficiency in buildings via providing the end-user with appliance-level consumption fingerprints.…”

Section: Fusion Of 2d Descriptionsmentioning

confidence: 99%

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Himeur,

Alsalemi,

Al-Kababji

et al. 2020

Preprint

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Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, applying the appropriate data fusion strategy to design an efficient energy efficiency system is not straightforward; it requires a priori knowledge of existing fusion strategies, their applications and their properties. To this regard, seeking to provide the energy research community with a better understanding of data fusion strategies in building energy saving systems, their principles, advantages, and potential applications, this paper proposes an extensive survey of existing data fusion mechanisms deployed to reduce excessive consumption and promote sustainability. We investigate their conceptualizations, advantages, challenges and drawbacks, as well as performing a taxonomy of existing data fusion strategies and other contributing factors. Following, a comprehensive comparison of the state-of-theart data fusion based energy efficiency frameworks is conducted using various parameters, including data fusion level, data fusion techniques, behavioral change influencer, behavioral change incentive, recorded data, platform architecture, IoT technology and application scenario. Moreover, a novel method for electrical appliance identification is proposed based on the fusion of 2D local texture descriptors, where 1D power signals are transformed into 2D space and treated as images. The empirical evaluation, conducted on three real datasets, shows promising performance, in which up to 99.68% accuracy and 99.52% F1 score have been attained. In addition, various open research challenges and future orientations to improve data fusion based energy efficiency ecosystems are explored.Keywords Data fusion • energy efficiency • sensors • appliance identification • fusion of 2D descriptors • machine learning.

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“…While most of the research efforts have been conducted towards developing and improving new technologies and materials that reduce wasted energy and promote energy saving, human-related aspects, especially those related to end-user's behavior [18] have received less attention. Therefore, strategies and objectives must be set in order to shape the behaviors of buildings' end-users and owners [19].…”

Section: Introductionmentioning

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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On the Applicability of 2D Local Binary Patterns for Identifying Electrical Appliances in Non-intrusive Load Monitoring

Cited by 15 publications

References 32 publications

Non‐intrusive load monitoring by voltage–current trajectory enabled asymmetric deep supervised hashing

Non‐intrusive load monitoring by voltage–current trajectory enabled asymmetric deep supervised hashing

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

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

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