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A novel non-intrusive load monitoring technique using semi-supervised deep learning framework for smart grid

Building Simulation 2024年第3期17卷 441-457页

作者： Mohammad Kaosain Akbar Manar Amayri nizar bouguila Concordia Institute for Information Systems Engineering Concordia UniversityMontrealCanada

Non-intrusive load monitoring(NILM)is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial *** plays a pivotal role in modernizing building energy management by disaggregating total energy consumption into individual appliance-level *** enables informed decision-making,energy optimization,and cost ***,NILM encounters substantial challenges like signal noise,data availability,and data privacy concerns,necessitating advanced algorithms and robust methodologies to ensure accurate and secure energy disaggregation in real-world *** learning techniques have recently shown some promising results in NILM research,but training these neural networks requires significant labeled *** initial sets of labeled data for the research by installing smart meters at the end of consumers’appliances is laborious and expensive and exposes users to severe privacy *** is also important to mention that most NILM research uses empirical observations instead of proper mathematical approaches to obtain the threshold value for determining appliance operation states(On/Off)from their respective energy consumption *** paper proposes a novel semi-supervised multilabel deep learning technique based on temporal convolutional network(TCN)and long short-term memory(LSTM)for classifying appliance operation states from labeled and unlabeled *** two thresholding techniques,namely Middle-Point Thresholding and Variance-Sensitive Thresholding,which are needed to derive the threshold values for determining appliance operation states,are also compared *** superiority of the proposed model,along with finding the appliance states through the Middle-Point Thresholding method,is demonstrated through 15%improved overall improved F1micro score and almost 26%improved Hamming loss,F1 and Specificity score for the p

关键词： semi-supervised learning non-intrusive load monitoring middle-point thresholding deep learning TCN LSTM

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Effective Frameworks Based on Infinite Mixture Model for Real-World Applications

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Computers, Materials & Continua 2022年第7期72卷 1139-1156页

作者： Norah Saleh Alghamdi Sami Bourouis nizar bouguila Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia College of Computers and Information Technology Taif UniversityTaif21944Saudi Arabia The Concordia Institute for Information Systems Engineering(CIISE) Concordia UniversityMontrealQC H3G 1T7Canada

Interest in automated data classification and identification systems has increased over the past years in conjunction with the high demand for artificial intelligence and security *** particular,recognizing human activities with accurate results have become a topic of high *** the current tools have reached remarkable successes,it is still a challenging problem due to various uncontrolled environments and *** this paper two statistical frameworks based on nonparametric hierarchical Bayesian models and Gamma distribution are proposed to solve some realworld *** particular,two nonparametric hierarchical Bayesian models based on Dirichlet process and Pitman-Yor process are *** models are then applied to address the problem of modelling grouped data where observations are organized into groups and these groups are statistically linked by sharing mixture *** choice of the Gamma mixtures is motivated by its flexibility for modelling heavy-tailed *** addition,deploying the Dirichlet process prior is justified by its advantage of automatically finding the right number of components and providing nice ***,a learning step via variational Bayesian setting is presented in a flexible *** priors over the parameters are selected appropriately and the posteriors are approximated effectively in a closed *** results based on a real-life applications that concerns texture classification and human actions recognition show the capabilities and effectiveness of the proposed framework.

关键词： Infinite Gamma mixture model variational Bayes hierarchical Dirichlet process Pitman-Yor process texture classification human action recognition

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