Adaptaciones a ADD (ADD-A)

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with future weather data have been published in a number of studies (Jentsch & Bahaj 2008; Du et al. 2010; Hacker & Holmes 2005; Hacker et al. 2009; Jenkins et al. 2011). This is a useful way of looking into the resilience of building design to projected changes in the climate. CIBSE future weather years (CIBSE & Met Office 2009) were used in the work of (Du et al. 2010), where a range of buildings of different types were modelled with UKCIP02 scenarios. These weather files make use of the morphing procedure which was also used by Hacker & Holmes (2005) for CIBSE TM36. 13 case study buildings, some with adaption strategies such as advanced natural ventilation systems, were investigated to study building performance with future weather data. The results suggested that for dwellings with very good control of solar shading, ventilation and internal heat gains, thermal comfort targets can be met until the 2050s. For office buildings, modelling suggested that it would be difficult to meet the thermal performance targets as the climate warms using passive measures alone (Jentsch & Bahaj 2008).

Future performance analysis can be used to determine the effectiveness of mitigation measures such as reducing internal heat gains. The importance of internal heat gains was highlighted in the work of (Jenkins 2009) where esp-r models of office buildings were used with morphed weather data. It was shown that reductions in internal heat gains could

significantly reduce cooling loads and might alleviate problems due to climate change. A zero cooling office building in London was said to be very difficult to achieve, even with internal heat gain minimisation. The options for producing future weather data were summarised in the work of Jentsch & Bahaj (2008). These authors also introduced a tool to aid the

implementation of the morphing procedure (described in section 2.2.2.1). Morphed weather data was used to investigate the future performance of a case study building, the Faraday Tower in the University of Southampton was used. It was found that with the full use of solar

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shading and night natural ventilation, the future climate conditions likely to be experienced towards the end of the building‘s design life should be addressed. A detailed monitoring regime was implemented for the tower with logged data at 5 minute intervals being collected. The simulation results were compared with this logged data and it was found that

performance under current extreme summer conditions can indicate future summer

performance. To determine what contribution noise reduction measures could play in climate change mitigation is a central goal of this thesis.

Simplifying future performance analysis is an attractive goal. Coley & Kershaw (2010) found a linear relationship between external climate and the internal temperature of buildings, for a wide range of building models representing an array of architecture and building use types such as domestic house, school apartment and office, over a range in thermal mass, glazing, U-value and ventilation. With the different weather files used, which included extreme future scenarios, 400 different combinations of building model and morphed weather file were tested. The resulting linear relationships enabled the authors to produce climate change amplification factors which they said ―fully describe the change in the internal environment of an architecture given a seasonal or annual change in external climate‖. This could greatly simplify calculations used to judge the resilience of particular designs and aid the future proofing of buildings in response to climate change. These linear relationships were confirmed in the study by (Du et al. 2010), which, in addition to domestic buildings, also found the same applied to a wide range of no-domestic buildings. The results were also said to give an indication of when additional mechanical cooling might need to be adopted. These linear relationships between average internal and external temperatures could be linked to building energy use. Whether these relationships continue to hold when probabilistic

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projections are used is not clear, or when the more complex issue of thermal comfort is considered.

Fewer building performance studies have been published using the UKCP09 dataset than with UKCIP02. This is due to the more recent release of the UKCP09 projections and also the difficulty in making this data usable for building simulation, such as the calculation of wind speed from PET as described in Section 2.2.3.2. One example by Hanby & Smith (2012) presented an analysis of the probabilistic scenarios released under UKCP09, together with a detailed building plant simulation of case a study building with evaporative cooling systems. It was found there was a high probability that evaporative cooling will still be a viable technique in to the 2050s. Results which have used the more recent UKCP09 scenarios were also presented by (Du et al. 2011). In this study, future TRYs were produced from the UKCP09 weather generator and used in EnergyPlus dynamic building simulations.

Interestingly the authors found that the prediction from UKCIP02 and UKCP09 gave similar building performance. This was the case, even though very different techniques were used to produce the future weather data for each set (the UKCIP02 set applied the morphing method and UKCP09 set was derived from the stochastic weather generator).

The UKCP09 weather generator makes available for download 3000 (30 years x100 runs) of weather data. To enable this to be used for dynamic building simulation some kind of simplification is required. This inevitably causes a tension between practical considerations and ease of use for industry practitioners against the fullest representation of climate

conditions and associated uncertainties. In the work of (Du et al. 2011) traditional single-year TRYs are produced, whereas other studies suggest a different use of the probabilistic

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information. For example the method proposed by (Jenkins et al. 2011) involves the use of a large database of climate data. This is available from the weather generator and is used to produce a probability curve of future overheating risk for a building. It aims to transfer the uncertainty represented in the probabilistic projections through into the building performance predictions. The method aims to produce probability curves to represent such things as overheating risk that can be considered as part of building design. The steps of the method are as follows:

 100 randomly chosen years of climate chosen for a given climate scenario.

 Hourly temperature is obtained from building simulation with these 100 years.

 Regression relationships obtained between hourly climate data and hourly internal temperatures by principle component analysis.

Part of the motivation for this was said to be that deterministic representation of the future climate such as that given by the morphing method, imply a level of certainty about the future climate that does not really exist. With this release of the probabilistic information in

UKCP09, an indication of the level of certainty is given. With the method proposed by this paper the uncertainty is transferred down through to the building simulation results. It is not clear though whether this approach will become a widely used part of the design process.

This method is somewhat in contrast to the use of UKCP09 weather data for building

simulation represented in (Watkins et al. 2011; Eames et al. 2011a), where yearlong data files were produced. In the work of Kershaw & Coley a separate yearlong weather file for a

number of climate percentile was produced so. Typical questions of concern to practising building services engineers are, for example, the hours over 28°C and annual energy use. Single-year reference years produce distributions for these parameters that show a good

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match with the full 3000 set according to (Kershaw et al. 2011). However, it was said by Watkins et al. (2011) that the use of a large number of weather files was not necessarily a benefit. Providing an excess of options to building designers could defeat the object of a TRY year, which was to provide a common practical weather year that all designers can use to compare building designs.