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2D Transformations of Energy Signals for Energy Disaggregation
Pascal Schirmer,
Iosif Mporas
Centre for Engineering Research
BioEngineering
Communications and Intelligent Systems
Centre for Future Societies Research
School of Physics, Engineering & Computer Science
Department of Engineering and Technology
Research output
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Contribution to journal
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Article
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peer-review
7
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Chemistry
Sampling
89%
Chemical Transformation
68%
Energy Consumption
59%
Energy
51%
Voltage
39%
Time
38%
Engineering & Materials Science
Time series
46%
Imaging techniques
41%
Sampling
37%
Smart meters
33%
White noise
29%
Reactive power
25%
Convolutional neural networks
24%
Deep learning
24%
Electric power utilization
19%
Energy utilization
19%
Monitoring
15%
Electric potential
13%
Physics & Astronomy
signatures
52%
performance
47%
sampling
36%
energy
31%
evaluation
27%
energy consumption
25%
learning
23%
white noise
22%
imaging techniques
21%
estimates
12%
electric potential
11%
Medicine & Life Sciences
Noise
100%
Deep Learning
37%