3.1. Aims
Following on from the literature review findings, particularly those summarised in section 2.5, the key aims of the study were as follows:
1. To develop understanding of the determinants of operational energy use of higher education buildings and in turn how these may be used to assess existing energy performance and the scope for reduction through redevelopment.
2. To measure the effect of redevelopment scenarios – both refurbishment and new-build – on the operational carbon impact of a building and to provide generalised findings that may be applied to the wider higher education building stock.
3. To measure the effect of redevelopment scenarios on embodied carbon impact with consideration of analysis uncertainties and to provide generalised findings that may be compared with operational carbon impacts.
3.2. Approach
Figure 3.1 summarises the approach taken to address the key aims of the study in section 3.1. In response to aim 1, and the need for a comprehensive, up-to-date analysis of university building energy use in section 2.4.1, a top-down building stock analysis approach was applied (work section 1 in Figure 3.1). A primary database was developed of annual energy use and associated building parameters for 1,950 English and Welsh higher education buildings. The database was based on data collected under the Display Energy Certificate (DEC) scheme, supplemented with parameters on building activity, context and local weather.
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Figure 3.1 Flow diagram showing the main work sections of the study, interrelationships and key outputs
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A secondary database, formed as a sub-set of 519 buildings in the primary database, containing a variety of other parameters describing building geometry and age was also developed (work section 2 in Figure 3.1). Statistical analysis was carried out to assess the impact of specific building parameters as energy determinants for both databases. In line with the findings in section 2.4.3, to explore more complex relationships, a novel application of ANNs was employed for analysing the secondary. The multivariate, machine-learning method provided combined analysis of all building parameters simultaneously. This allowed observations to be made both on the building parameters and on the efficacy of the method.
In response to aims 2 and 3 in section 3.1 and the findings from 2.4.4 , a bottom-up case study approach was first used (work section 3 in Figure 3.1). Operational and embodied carbon impacts of redevelopment scenarios for five case study higher education buildings were measured using modelling based on real monitoring data. Four case study buildings were at University College London (UCL) and one was at the Royal College of Art (RCA). The buildings were selected to cover a range of activities and to provide representation of buildings with high redevelopment potential. On-site monitoring was carried out at each building for a period of 12 months to collect data on the operational characteristics. Energy, geometry, fabric, systems and operational data were combined in operational and embodied carbon computer simulations, first to calibrate the base model and then to measure the impacts following hypothetical redevelopment scenarios.
To generalise findings in accordance with aims 2 and 3 in section 3.1 and the method review in section 2.4.5, an archetype-based method was taken (work section 4 in Figure 3.1). Archetype pre-1985 era11 higher education buildings were defined using data in the primary and secondary databases.
Archetypes were based on three principal activity groups and two forms of primary environmental strategy giving six archetypes in total. Distributions of results were obtained for each archetype by
11 1985 was used as a cut-off for building energy efficiency standards, as discussed in section 10.3.2.
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analysing two or three different sub-activities and two principal forms relating to urban and rural contexts (28 distinct models in total). It should be noted that, although two different forms were considered for the results distribution, the archetypes were primarily distinguished by activity and primary environmental strategy. Operational and embodied carbon simulations were built for each archetype and calibrated using energy data from the primary database and building data from the case study analysis.
To report the generalised findings, to explain the concepts and to assist in the decision-making phase, a demonstration visualisation tool was also developed (the visualisation output shown in Figure 3.1).
This allowed the operational carbon performance of existing estates buildings to be graded and the life cycle carbon impacts for potential refurbishment or redevelopment to be assessed. The aim was for the tool to be educational and to allow the scope of impacts of actual building refurbishment/replacement decisions to be determined. The tool would be used by both designers and estates managers in the early planning stages. Development of the tool was in line with requirements of the EngD to raise visualisation-related coding skills and to present research data graphically.
3.3. Limitations
The research was designed to provide effective responses to the aims with available data and resources, although this resulted in certain limitations which should be considered alongside the findings. The principal limitations are summarised as follows (with further discussion in the relevant methodology sections):
DEC data Whilst extensive in terms of the number of buildings, the DEC data only comprised English and Welsh buildings greater than 1,000m2 in area. The buildings
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incorporated were also subject to some self-selection owing to the degree of participation of individual higher education institutions.
Pre-1985 buildings
For analysis of the archetypes, a sub-set of the database for pre-1985 buildings was used. The redevelopment findings would be limited to this age group, however some principles may also be relevant to more recently constructed buildings.
Embodied carbon data
The embodied carbon analysis was carried out using a comprehensive embodied carbon database that was compliant with BS EN 15978:2011 standards, although it was based on generic material data. There may be some variation where specific products or alternative data are considered.
Simulation of operational carbon impact
The operational carbon impact was assessed using dynamic thermal simulation methods. The tool used was industry-standard and has complied with third-party vertification, although the underlying calculation methods could vary relative to other tool providers and it is noted that generally that such tools provide a simplification of the phenomena that exist in practice.
Building design schemes
To give a range of results, a broad selection of building design – architectural, structural and building services – schemes was incorporated, typically based on those observed in the case study buildings. However, these were not comprehensive and different results may be obtained for other schemes.
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