Figure 7.6 shows the median change in energy use measured for each intervention across all of the trained ANNs. As shown, 95% confidence intervals around the median were also obtained by bootstrapping. The changes in output were considered significant (either positively or negatively) where the interval excluded zero i.e. there was considered to be 95% likelihood that the true median sat only above or only below zero.
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Figure 7.6 Intervention analysis results: median change in energy use for each intervention by activity type with 95%
confidence intervals All values are median change in output (%)
-40 4a. Reduce occupied hours by 20%
-40 4b. Reduce occupied hours by 40%
-40 5a. Increase glazing ratio by 10%
-40 5b. Reduce glazing ratio by 10%
All values in the charts are median change in output (%)
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For the conversion to natural ventilation (intervention 1), energy reductions of up to around 20% were found for all activities except residential and these were significant for both energy uses for academic lab/workshop buildings and for heating fuel use in administration buildings.
The wide-ranging response in residential buildings seems intuitive given that most residential buildings were naturally-ventilated so training examples were limited.
In all cases, significant increases of up to 10% were found for electricity use associated with the fabric upgrade scenario (intervention 2) using year of construction as a proxy. This appears to accord with the correlations for age found in Table 7.2. Significant reductions of up to 5% were found however for heating fuel use in academic non-lab/workshop and residential buildings.
Similar although stronger patterns were found for the double glazing upgrade scenario (intervention 3): for all building groups except residential buildings significant increases of between around 20 and 30% in electricity use were found and significant reductions in heating fuel use of greater than 10%
were found for both types of academic building. The higher reductions in heating fuel use relative to intervention 2 suggests that the double glazing parameter was a more reliable indicator of thermal performance than year of construction alone. However, the large increases found for electricity use by this intervention are difficult to relate to the use of double glazing alone. This suggests that within the training data the double glazing parameter was still highly correlated with building age so intervention 3 was showing a similar effect to intervention 2.
The responses to occupied hours reductions (20% and 40% as interventions 4a and 4b) were typically small, and less than the equivalent percent reduction in hours, although where they were significant they were usually negative. It is notable that electricity use in administration buildings was found to be the most sensitive to occupied hours, possibly reflecting a higher proportion of electrical loads such as IT equipment that are more occupancy-related. Academic lab/workshop buildings showed the most
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significant reductions of heating fuel use which may be attributed to higher variation in occupancy, and therefore heating periods of such buildings.
For all building groups except academic lab/workshop, small and usually significant increases in both energy uses were found to be associated with the increase in glazing ratio, and vice versa (interventions 5a and 5b respectively). This generally reflects the correlation findings in Table 7.2 although does not accord well with associated theory, particularly for electricity use. It is possible that the correlations with height and floor area parameters observed in the principal component analysis and any corresponding influenced remained. For academic lab/workshop buildings, the trend is reversed however, suggesting a different relationship with glazing ratio for more intensively-serviced buildings.
Overall, the number of significant changes in output observed across the interventions suggests that the ANN models have successfully established some stable relationships based on the training data.
However, certain findings may be more reflective of the nature of the available training data and similarly the limitations of the ways in which can be presented to the model.
7.5. Summary
The key findings from the analysis of the secondary database were as follows:
- The distribution of building construction eras between pre- and post-war institutions largely accorded with the age of the institution (and in turn Russell Group membership), although the proportion of post-2000 buildings was found to be similar for both types of institution. A slight trend was observed for post-2000 buildings to be located in rural contexts.
- Significant negative correlations were observed between electricity use and building age, both in terms of specific age and era of construction. This trend was also observed in some cases at
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primary activity level. Although correlations between heating fuel and age were less strong, buildings constructed in the middle, 1950-1985 era were found to have the highest heating fuel use. At primary activity level, there was an overall trend of lower heating fuel use for post-1985 buildings relative to pre-1985 buildings.
- A number of key distinctions were observed between building geometry parameters. Relative to their rural counterparts, non-residential urban buildings were found to be significantly taller, greater in floor area, less detached and more shaded and to have higher aspect ratio, glazing ratio and greater use of double glazing. The same was found for residential buildings, although significant differences in glazing ratio were not found and rural buildings were found to have significantly greater use of double glazing.
- Significant positive correlations were found between electricity use and floor area, height, glazing ratio and occupied hours for most building groups analysed and also with south and west shading factors for residential buildings. However, with the exception of occupied hours, relatively few linear or monotonic correlations between heating fuel use and the building parameters were found.
- An investigation to test the application of an ANN model to relate building energy use to the multivariate building parameters demonstrated success in terms of reduction of the associated generalisation error relative to a benchmarking approach. For all four activity groups assessed the generalisation error reduced significantly as input parameters were added to the model.
The lowest mean generalisation errors across all activities were 26% for electricity use and 28%
for heating fuel use.
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- An intervention analysis carried out on the trained ANN models demonstrated a number of significant changes in output in response to input changes, indicating a stable response of the base ANN models. This suggested some effectiveness of the ANN method.
- The intervention analysis results showed significant changes in energy use for certain activities for all interventions assessed. Interventions with the largest and most significant energy changes were conversion to natural ventilation and upgrading to double glazing. Other significant energy changes of greater than 10% were observed, for example electricity use in administration buildings when changing occupied hour and glazing ratio. Overall, academic lab/workshop buildings and administration buildings appeared to be the most responsive to the interventions in terms of significant changes.
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