Occupancy-based energy consumption modelling using machine learning algorithms for institutional buildings

Author:

Anand Prashant,Deb Chirag,Yan Ke,Yang Junjing,Cheong David,Sekhar Chandra

Funder

National University of Singapore

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Building and Construction,Civil and Structural Engineering

Reference62 articles.

1. I. Energy Agency, Policy Pathways Modernising Building Energy Codes, (2013). http://www.iea.org/publications/freepublications/publication/PolicyPathwaysModernisingBuildingEnergyCodes.pdf (accessed December 13, 2017).

2. D.L. U.S.Departmentofenergy, 2011 Buildings Energy Data Book, March. (2012). http://buildingsdatabook.eere.energy.gov (accessed August 15, 2016).

3. Anand P., Cheong D., Sekhar C., An interaction between occupant related energy consumption among different spaces |ASHRAE Annual Conference, Houstan (2018) https://www.researchgate.net/publication/326231770_An_Interaction_between_Occupant_Related_Energy_Consumption_among_different_Spaces (accessed August 10, 2019)

4. Anand, P., Sekhar, C., Yang, J., Cheong, D., Improving The Accuracy Of Building Energy Simulation Using Real-Time Occupancy Schedule And Metered Electricity Consumption Data. In Proceedings of the 2017 ASHRAE Annual Conference, Singapore, 6 June 2017.

5. Occupancy-based zone-level VAV system control implications of thermal comfort, ventilation, indoor air quality and building energy efficiency;Anand;Energy Build.,2019

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