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Abdullah Alsalemi
Researcher at Qatar University
Publications - 79
Citations - 1505
Abdullah Alsalemi is an academic researcher from Qatar University. The author has contributed to research in topics: Efficient energy use & Energy consumption. The author has an hindex of 16, co-authored 66 publications receiving 715 citations. Previous affiliations of Abdullah Alsalemi include De Montfort University.
Papers
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Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives
TL;DR: An in-depth review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence is presented, in which a comprehensive taxonomy is introduced to classify existing algorithms based on different modules and parameters adopted.
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Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree
TL;DR: A novel non-intrusive appliance recognition system based on detecting events in the aggregated power signal using a novel and powerful scheme, applying multiscale wavelet packet tree to collect comprehensive energy consumption features, and adopting an ensemble bagging tree classifier is proposed.
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A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks
TL;DR: Experimental results on simulated and real datasets collected at two regions, which have extremely different climate conditions, confirm that the proposed deep micro-moment architecture outperforms other machine learning algorithms and can effectively detect anomalous patterns.
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Building power consumption datasets: Survey, taxonomy and future directions
TL;DR: A novel visualization strategy based on using power consumptionmicro-moments has been presented along with an example of deploying machine learning algorithms to classify the micro-moment classes and identify anomalous power usage.
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A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects
Yassine Himeur,Abdullah Alsalemi,Ayman Al-Kababji,Faycal Bensaali,Abbes Amira,Christos Sardianos,George Dimitrakopoulos,Iraklis Varlamis +7 more
TL;DR: This paper presents the first timely and comprehensive reference for energy-efficiency recommendation systems and provides 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.