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A synergistic approach using digital twins and statistical machine learning for intelligent residential energy modelling.

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第一作者： Ahmad,Almadhor

第一单位： Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, 72388, Sakaka, Saudi Arabia. aaalmadhor@ju.edu.sa.

作者： Ahmad,Almadhor ^[1] ; Shtwai,Alsubai ^[2] ; Natalia,Kryvinska ^[3] ; Nejib,Ghazouani ^[4] ; Belgacem,Bouallegue ^[5] ; Abdullah,Al Hejaili ^[6] ; Gabriel Avelino,Sampedro ^[7]

作者单位： Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, 72388, Sakaka, Saudi Arabia. aaalmadhor@ju.edu.sa. ^[1] College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, 16273, AlKharj, Saudi Arabia. ^[2] Department of Information Management and Business Systems, Faculty of Management, Comenius University Bratislava, Odbojarov 10, Bratislava 25, Bratislava, 82005, Slovakia. natalia.kryvinska@uniba.sk. ^[3] Mining Research Center, Northern Border University, 73213, Arar, Saudi Arabia. ^[4] Department of Computer Engineering, College of Computer Science, King Khalid University, 61421, ABHA, Saudi Arabia. ^[5] Faculty of Computers & Information Technology, Computer Science Department, University of Tabuk, 71491, Tabuk, Saudi Arabia. ^[6] Department of Computer Science, University of the Philippines Diliman, Quezon City, 1101, Philippines. ^[7]

关键词 Artificial intelligence (AI)Digital twin Energy efficiency IoT sensor data Machine learning Real-time monitoring

DOI 10.1038/s41598-025-09760-y

PMID 40681639

发布时间 2025-07-22

基金项目

NBU-FFR-2025-2105-XX/Deanship of Scientific Research at Northern Border University RGP2/307/45/Deanship of Scientific Research, King Khalid University

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准确一般字义偏差语意偏离译文作用小提交