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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| Format: | Conference or Workshop Item |
| Language: | English English |
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IEEE
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/38589/ 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 |
| _version_ | 1848825545750478848 |
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| author | Akhtar, Shamim Muhamad Zahim, Sujod Hussain Rizvi, Syed Sajjad |
| author_facet | Akhtar, Shamim Muhamad Zahim, Sujod Hussain Rizvi, Syed Sajjad |
| author_sort | Akhtar, Shamim |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:30:38Z |
| format | Conference or Workshop Item |
| id | ump-38589 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:30:38Z |
| publishDate | 2021 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-385892023-09-08T03:42:41Z http://umpir.ump.edu.my/id/eprint/38589/ A Novel Deep Learning Architecture for Data-Driven Energy Efficiency Management (D2EEM) - Systematic Survey Akhtar, Shamim Muhamad Zahim, Sujod Hussain Rizvi, Syed Sajjad QA76 Computer software T Technology (General) TK Electrical engineering. Electronics Nuclear engineering 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. IEEE 2021 Conference or Workshop Item PeerReviewed pdf en 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 pdf en 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 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 (Published) https://doi.org/10.1109/ICEET53442.2021.9659737 |
| spellingShingle | QA76 Computer software T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Akhtar, Shamim Muhamad Zahim, Sujod Hussain Rizvi, Syed Sajjad A Novel Deep Learning Architecture for Data-Driven Energy Efficiency Management (D2EEM) - Systematic Survey |
| title | A Novel Deep Learning Architecture for Data-Driven Energy Efficiency Management (D2EEM) - Systematic Survey |
| title_full | A Novel Deep Learning Architecture for Data-Driven Energy Efficiency Management (D2EEM) - Systematic Survey |
| title_fullStr | A Novel Deep Learning Architecture for Data-Driven Energy Efficiency Management (D2EEM) - Systematic Survey |
| title_full_unstemmed | A Novel Deep Learning Architecture for Data-Driven Energy Efficiency Management (D2EEM) - Systematic Survey |
| title_short | A Novel Deep Learning Architecture for Data-Driven Energy Efficiency Management (D2EEM) - Systematic Survey |
| title_sort | novel deep learning architecture for data-driven energy efficiency management (d2eem) - systematic survey |
| topic | QA76 Computer software T Technology (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/38589/ http://umpir.ump.edu.my/id/eprint/38589/ 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 |