Use this URL to cite or link to this record in EThOS:
Title: Modelling thermal loads for a non-domestic building stock : associating a priori probability with building form and construction : using building control laws and regulations
Author: Smith, Stefan Thor
ISNI:       0000 0004 2685 7440
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2009
Availability of Full Text:
Access from EThOS:
Access from Institution:
Building Energy Assessment at stock level is an important task in identifying the best strategies for achieving a more energy efficient and low carbon society. Non-domestic buildings are identified to make up 17% of total energy consumption in England and Wales and 19% of CO2 emissions. To understand the energy requirement of the non-domestic stock, large scale (empirically based) energy surveying has been carried out namely in the Non-Domestic Building Stock project and Carbon Reductions in Buildings project. It is recognised that building energy surveys are difficult to carry out; expensive on time, technical resources, and metered energy use is (on a large scale) necessarily crude. With improving computer ability, dynamic energy modelling tools allow for detailed assessment of building energy use and comfort performance. Using Monte Carlo simulation a method of assessing the probable variability in non-domestic building thermal energy loads was developed. The method was developed to capture the heterogeneity in non-domestic buildings at national stock level and determine how stock level physical form variations impact thermal loading. Non-domestic building form and surrounding topography are considered to be influenced by building control laws and building regulations. Control documentation often stipulates guidelines and best practice - hence building heterogeneity. As such, historical regulations were used to develop basic probability distributions of potential physical characteristics associated with non-domestic buildings. Stating that form and site characteristics are randomly determined from the defined probability distributions, a stochastic modelling process to represent thermal variation in a building stock was developed. This provided potential for categorising building thermal performance by period of construction. The model utilised a dynamic simulation model as a 'black-box' for predicting base thermal loads.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: NA Architecture