Developing Data-Driven Occupancy Detection Models Based on Individual Plug Load Profiles in Office Spaces-Reference-Cited by-同舟云学术

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Developing Data-Driven Occupancy Detection Models Based on Individual Plug Load Profiles in Office Spaces

Published:2024-01-25 Issue: Volume:90 Page:979-986
ISSN:
Container-title:Computing in Civil Engineering 2023
language:
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Author:

Vosoughkhosravi Sorena¹²³,Thomas Madeline A.¹²³,Jafari Amirhosein¹²³

Affiliation:

1. Ph.D. Student, Bert S. Turner Dept. of Construction Management, Louisiana State Univ., Baton Rouge, LA.

2. Undergraduate Student, Division of Electrical and Computer Engineering, Louisiana State Univ., Baton Rouge, LA.

3. Assistant Professor, Bert S. Turner Dept. of Construction Management, Louisiana State Univ., Baton Rouge, LA.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485248.117

Reference20 articles.

1. Agarwal Y. Balaji B. Gupta R. Lyles J. Wei M. and Weng T. (2010). “Occupancy-driven energy management for smart building automation.” BuildSys’10 - Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings.

2. Electricity consumption forecasting models for administration buildings of the UK higher education sector;Amber K. P.;Energy and Buildings,2015

3. ASHRAE. (2018). “ASHRAE Global Occupant Behavior Database: Plug Load.” .

4. Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings;Chae Y. T.;Energy and Buildings,2016

5. Chen D. Barker S. Subbaswamy A. Irwin D. and Shenoy P. (2013). “Non-intrusive occupancy monitoring using smart meters.” BuildSys 2013 - Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings.

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