Machine Learning-Based Power Estimation is a power estimation methodology that employs machine learning models to predict system or component power consumption based on operational and behavioral data, distinguishing itself from traditional analytical or simulation-based approaches by leveraging learned patterns from empirical observations.
241
Publications
12.9K
Citations
893
Authors
402
Institutions
Publications per year
2017–2026
184
Parents
Publications
241
1–5 of 241
Authors
893
- AG
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1–5 of 893
Institutions
402
The University of Texas at Austin
Austin, United States
6 h-index
16 publications
760 citations
University of Tehran
Tehran, Iran
5 h-index
11 publications
538 citations
Tsinghua University
Beijing, China
5 h-index
12 publications
552 citations
University of Illinois Chicago
Chicago, United States
4 h-index
14 publications
566 citations
City University of Hong Kong
Hong Kong, Hong Kong
4 h-index
8 publications
478 citations
University of California San Diego
San Diego, United States
4 h-index
13 publications
665 citations
South China University of Technology
Guangzhou, China
3 h-index
9 publications
369 citations
Columbia University
New York, United States
3 h-index
10 publications
244 citations
1–8 of 402
Venues
- Applied Energy
12 publications · 1.5K citations
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- Energies
9 publications · 475 citations
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- Renewable Energy
8 publications · 758 citations
8
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- Energy
7 publications · 647 citations
7
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- Energy Reports
5 publications · 288 citations
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