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Augmenting building energy usage models with data segmentation
William Mounter
,
Huda Dawood
,
Nashwan Dawood
SCEDT Engineering
SCEDT School Executive Team
Centre for Sustainable Engineering
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
126
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Dive into the research topics of 'Augmenting building energy usage models with data segmentation'. Together they form a unique fingerprint.
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Keyphrases
Activity Period
16%
ANN Model
16%
Artificial Neural Network
16%
Building Activity
16%
Building Construction
16%
Building Energy Consumption
100%
Building Energy Prediction
16%
Building Management System
16%
Clarendon
16%
CO2 Pollution
16%
Computation Time
16%
Data Segmentation
100%
Data-driven Model
33%
Dormancy Period
16%
Energy Conservation
16%
Energy Management
16%
Energy Prediction
16%
Energy Usage
16%
Ensemble Model
16%
Facility Managers
16%
Global Energy
16%
Model Accuracy
16%
Modern Civilization
16%
Network Data
16%
Neural Network
16%
Operational Period
16%
Usage Model
100%
User Behavior
16%
Engineering
Artificial Neural Network
50%
Building Construction
25%
Building Management System
25%
Computational Time
25%
Energy Building
100%
Energy Engineering
50%
Energy Management
25%
Energy Usage
100%
Global Energy
25%