A Novel Deep Learning Architecture for Data-Driven Energy Efficiency Management (D2EEM) - Systematic Survey

Akhtar, Shamim and Muhamad Zahim, Sujod and Hussain Rizvi, Syed Sajjad (2021) A Novel Deep Learning Architecture for Data-Driven Energy Efficiency Management (D2EEM) - Systematic Survey. In: 7th International Conference on Engineering and Emerging Technologies, ICEET 2021 , 27-28 October 2021 , Istanbul, Turkey. (176217). ISBN 9781665427142

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Abstract

The Energy Management System (EMS) is the cost-effectiveness, robustness, and flexible approach for energy efficiency management (EEM). Data-Driven Energy Efficiency Management (D2EEM) is a recent advancement in EMS. The D2EEM is the blend of data science and artificial intelligence for EEM. Due to the highly tolerant to the performance plateau and unconstraint to the feature extraction, Deep Learning (DL) facilitates handling big data-driven problems of EEM. To the best of the knowledge, the accurate and robust D2EEM is the pressing need. Moreover, the accurate pre-trained DL network for EEM is not available in the recent literature. In this work, a comprehensive study is presented to devise a D2EEM. Moreover, the architecture is suggested in connection to the research gap.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Data driven; Deep learning; Energy efficiency; Energy management; Machine learning
Subjects:	Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	Institute of Postgraduate Studies College of Engineering
Depositing User:	Miss Amelia Binti Hasan
Date Deposited:	08 Sep 2023 03:42
Last Modified:	08 Sep 2023 03:42
URI:	http://umpir.ump.edu.my/id/eprint/38589
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