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Download fileCoupling a stochastic occupancy model to EnergyPlus to predict hourly thermal demand of a neighbourhood
conference contribution
posted on 2016-05-11, 13:01 authored by Candy He, Timothy Lee, Simon Taylor, Steven FirthSteven Firth, Kevin LomasKevin LomasWhen designing and managing integrated renewable
energy technologies at a community level, prediction
of hourly thermal demand is essential. Dynamic
thermal modelling, using deterministic occupancy
profiles, has been widely used to predict the highresolution
temporal thermal demand of individual
buildings. Only in recent years has this approach
started to be applied to simulate all buildings in a
neighbourhood or an entire housing stock of a region.
This study explores the potential of predicting hourly
thermal demand for a group of dwellings by applying
a stochastic occupancy model to dynamic thermal
modelling. A case study with 125 new houses
demonstrates the approach. The result was a more
realistic and representative hourly thermal demand
profile, compared to using standard deterministic
occupancy profiles.
Funding
This work was funded under UK EPSRC grant EP/I002124/1 (Self Conserving Urban Environments – SECURE). SECURE is a consortium of four UK universities: Newcastle, Sheffield, Exeter and Loughborough. Website: https://www.secureproject. org/
History
School
- Architecture, Building and Civil Engineering
Published in
14th Conference of International Building Performance Simulation Association (BS2015) http://www.ibpsa.org/?page_id=619Pages
2101 - 2108 (8)Citation
HE, M. ... et al, 2015. Coupling a stochastic occupancy model to EnergyPlus to predict hourly thermal demand of a neighbourhood. IN: Mathur, J. and Garg, V. (eds). Proceedings of the 14th International Conference of the International Building Performance Simulation Association (BS2015), 7th-9th December 2015, Hyderabad, India, pp. 2101 - 2108.Publisher
© International Building Performance Simulation Association (IBPSA)Version
- VoR (Version of Record)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Acceptance date
2015-09-03Publication date
2015Notes
This is a conference paper.ISBN
9789352301188Publisher version
Language
- en