Parameter Values

Theoretically derived parameter distributions were used in light of little stock level information regarding building characteristics and structural properties associated with period of construction. The theoretical assessment resulted in a stochastic modelling process by use of Monte Carlo method. It does not allow the results to be considered as a precise description of reality, but rather a statistical measure. The approach, therefore, provides a statistical measure of how period of construction influences thermal loading.

The confidence of the model results is dependent on the confidence applied to the theoretical parameter distributions. A measure of confidence would result from comparison to real stock distributions, but as stated such real data is currently not available.

The ranges in parameter values were kept within physically reasonable limits, either set by control laws or set by the parameter definition (i.e. sky view factor of a vertical wall has limits of 0 to 0.5).

Building Component Form

It was demonstrated that dynamic thermal properties of composite building components are dependent on construction order and material used. This raised a concern of using steady-state properties (i.e.

U-value) in regulating a building’s thermal efficiency.

Building regulations and model by-laws were used to develop databases of building component construc-tions. The databases covered use of different materials and varied order of material layers for composite constructions. By capturing potential variation in component construction the model considers varying

dynamic thermal properties, for otherwise constant steady-state properties. Each constructions database represented a different period of construction control for buildings built specifically for commercial office use.

These databases are not a comprehensive description of all feasible constructions, but rather a range of constructions that represent the potential variation in dynamic thermal behaviour. It is, therefore, not considered necessary (though desirable) to expand each constructions database to cover all potential and known component construction forms.

Beyond identifying glazing types the ratio of windows to wall area is important in terms of solar loading, daylight and heat transfer and storage. The impact of daylight on artificial light requirements was not represented in the model.

Regulations, model by-laws and daylight standards were examined to determine modal values and phys-ical limitations in window-wall ratios. A triangular distribution was used in all cases from which a random window-wall ratio would be applied. As the building models were all free standing with four external walls, each wall was given the same ratio. This was recognised as a limitation in the model as, for example, if the building adjoins two others the exposed fa¸cade will necessarily have a higher window-wall ratio. This would particularly effect the lower window-window-wall limit of the considered distributions;

regulations stipulate a minimum window area of 10% of floor area.

The method taken in randomly determining window-wall ratio is limited by the limited database of building models.

In all instances of a pitched roof the resulting roof void was considered as an unoccupied zone with free floating temperature. The variation in roofing types were limited in the modelling to flat and pitched-gable-ended roof types. A sensitivity study showed that for an uncontrolled roof void these two roof types were an adequate representation of roof types in the building stock².

In all, the model captures variation in building component form that is significant to the varying thermal behaviour³ of buildings in the non-domestic office stock of England and Wales. The application of probability distributions to the occurrence of these different forms in the stock have been limited to theoretical prediction. These predictions being governed by application of economic reasoning to physical and regulated limitations in building form.

Adventitious Leakage

Uncontrolled air leakage in a building is highly variable; a result of building design, maintenance, build quality and construction methods. Literature review highlighted insufficient data is available to associate expected leakage rates with different building types. The types being categorised by use, construction method and region, for example.

A new modelling approach was taken to associate adventitious leakage rates with a building. By applying a probable component leakage to a building described by its component parts, a statistical evaluation of expected leakage could be given. This method relied on existing experimental data on component leakage rates to which a normal distribution was assumed.

2As applied to the dynamic thermal modelling tool of esp-r.

3The thermal behaviour as determined by the building structure and form - i.e. passive thermal behaviour.

This novel method allowed air leakage rates to be applied to the stochastic modelling process undertaken in the thesis. The method showed a high degree of variation, not only in average leakage rates for feasible building types but in variance of the probability distribution of buildings displaying similar average leakage rates. This provided supporting evidence to the difficulty (reported in the reviewed literature) of associating building types with air leakage.

The advantage of this component leakage model over other methods is that it not only accounts for the variation in component form of a building stock, but it also provides a statistical evaluation of adventitious leakage for every considered building form. There is no requirement for categorising buildings to offer a statistical prediction according to some predefined building type.

The value of this method is limited by:

(i) the level of component leakage data (ii) the data is not specific to one country

(iii) much of the data result from laboratory based testing and

(iv) the sample size of tested components are (in some instances) very small.

The model would become more comprehensive with further leakage testing of the considered building components along with incorporating other component types.

Weather Data

The weather data was limited in value as was:

(i) geographically limited - limited number of weather files used (ii) does not offer an account of statistical variability in weather

(iii) the TRYs are based on 1961-1990 recorded data - does not represent future (changing) climate

No account was made for determining the geographical distribution of building stock in relation to period of construction; an equal distribution by construction period was assumed. With a higher resolution of weather locations a better approach would be to consider the proportion of stock (in each weather region) belonging to each period of construction control. By this downscaling the model could be used to provide regional policy. The use of future climate scenarios would also be more beneficial to understanding future energy demands of the existing building stock.

Radiative Factors

A theoretical approach to determining sky view factor (Sν) was examined to offer the probable variation associated with buildings constructed in different control periods. This method, in the first instance, was more applicable to earlier control periods than later periods as a ’closed’ (i.e. a street canyon) urban form was assumed. For later control periods the building controls⁴ encouraged more ’open’ form; so

contradicting the assumption of a street canyon in determining modal elevation angle and resulting Sν.

By sectioning of the 180^○ horizontal view plane some account of varying urban form was given.

Though no validation could be given for the model results (again a restriction of required scale of data for validation) the mean and modal Sν for each control period were similar to ’representative’ values published in energy modelling literature.

This approach does not account for urban redevelopment that result in mixed control regions and is limited in consideration of variable urban forms. The method applies a singular Sν to a building fa¸cade with no consideration for variation in the vertical or horizontal plane of the fa¸cade. For building energy modelling, however, this is an accepted practice (as stated in the EnergyPlus engineering reference manual). For the purposes of energy modelling this approach is adequate.