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Updated: Jul 19, 2025
05:50
Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger
Published on: January 16, 2020
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Zone-wise occupancy schedules developed using Time Use Survey data for building energy performance simulations
Divyanshu Sood1, Ibrahim Alhindawi2, Usman Ali1
1School of Mechanical and Materials Engineering and UCD Energy Institute, University College Dublin, Ireland.
Data in Brief
|August 14, 2023
Summary
This study created a UK occupancy profile dataset from Time Use Survey data to analyze how people impact home energy use. The profiles reveal realistic occupant behavior for better energy consumption assessments.
Area of Science:
- Building Science
- Energy Efficiency
- Human Behavior Studies
Background:
- Accurate energy consumption assessments require understanding occupant behavior.
- Existing data often lacks detailed, realistic occupancy patterns.
- Time Use Survey (TUS) data offers a rich source for behavioral analysis.
Purpose of the Study:
- To develop a comprehensive occupancy profile dataset using UK TUS 2014-15 data.
- To evaluate the impact of occupancy on energy consumption at multiple scales.
- To enable more accurate, context-specific energy assessments in dwellings.
Main Methods:
- Leveraged publicly available UK Time Use Survey (TUS) 2014-15 data.
- Employed data conversion, categorization, extraction, and analysis.
- Constructed multi-scale archetypes for spatial and temporal analysis.
Main Results:
- Generated a dataset of 5,376 realistic day-wise, zone-level occupancy profiles.
- Profiles account for dwelling type, occupant numbers, month, and day of the week.
- Dataset captures realistic occupant availability and behavior patterns.
Conclusions:
- The developed occupancy profiles provide valuable insights into dwelling energy usage.
- Enables more precise energy consumption modeling based on occupant behavior.
- Facilitates context-specific energy assessments for improved efficiency.
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