A Novel Deep Learning Architecture for Data-Driven Energy Efficiency Management (D2EEM) - Systematic Survey
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...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2021
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/38589/1/A%20Novel%20Deep%20Learning%20Architecture%20for%20Data-Driven%20Energy%20Efficiency%20Management%20%28D2EEM%29%20-%20Systematic%20Survey.pdf http://umpir.ump.edu.my/id/eprint/38589/2/IEEE_A_Novel_Deep_Learning_Architecture_for_Data-Driven_Energy_Efficiency_Management_D2EEM_-_Systematic_Survey.pdf http://umpir.ump.edu.my/id/eprint/38589/ https://doi.org/10.1109/ICEET53442.2021.9659737 |
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| Summary: | 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. |
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