Participantes: criterios de selección y compromisos adoptados

Researchers in Asia, notably at the City University of Hong Kong, have also contributed to insight on the impact of climate change on energy use in buildings. Research output from this university used, amongst other methods, the principle component analysis (PCA) to investigate the impact of climate change on a typical office building in Hong Kong (Lam et

al, 2010a). Lam et al (2010a) explained that this approach was superior to the degree day method, as the PCA allows the incorporation of other weather variables in addition to dry-bulb temperature, and that it was also better than multiple linear regression (MLR) analysis as this method can cope with multicollinearity¹ better. Their research indicated that dry-bulb temperature, wet-dry-bulb temperature and global solar radiation were the best predictors for their work leading to an estimate of an annual rise in cooling load of 9.1% translating into an increase in energy use of 4.3% for low radiative forcing (see Section 3.2.1 for an explanation of the term ‘radiative forcing’). The researchers also estimated the model error with the coefficient of variation of the root mean square error ( ) (explained further in Section 5.7) using data for 2006-2008. They found the ) for the heating load to range from 11.5% to 30.9% and for the cooling load to vary from 3.6% to 4.0%. A similar study on air-conditioning requirements in commercial buildings (Lam et al, 2010c) predicted a rise in electricity use for air conditioning of 18.4% for the 2069-2100 period (compared to 2008 consumption) for low radiative forcing. The ) of their model varies from 9.2% to 23.5%.

Another approach used at this university was the overall thermal transfer value (OTTV) which was used on residential buildings (Wong et al, 2010). The OTTV gauges the heat transfer from the outside of the building to the inside through the building envelope, or vice versa, taking into consideration both walls and fenestration. The researchers also included the evaluation of energy saving measures in their study indentifying an increase in thermal insulation as the most effective option. The results showed that the building cooling load was expected to increase by 12.3% (compared with the period between 1979 and 2008) for 2071-2100 for low radiative forcing. It should be pointed out that the normalisation was different from that used in Lam et al (2010c) and therefore results are not completely comparable.

Morphed weather files together with a building simulation software package (EnergyPlus) were used for another piece of research investigating an office building and a residential building under climate change at this university (Chan, 2011). The researcher predicted that the increase in air-conditioning energy requirements of the office building would rise by 9.9% for a low forcing emission scenario for 2080-2099 (when compared with results based on then present weather files), whereas for the residential building, demand was

- 14 -

expected to increase by 16.5% under the same conditions. The lower increase for the office building, the researcher surmised, would be due to the cooling load being significantly influenced by electrical equipment, which was not the case at the residential building.

A further paper from this university (Wan et al, 2011a) summarized work on predictions of the increase in average building energy use in Hong Kong with a PCA and a number of adaptation measures, which were simulated with VisualDOE4.1, relating to the building envelope, temperature set point and chiller efficiency. When using data from local weather stations and predictions based on MICRO3.2-H (Nozawa et al, 2007) the researchers found a continuous warming trend. This is expected to cause an electricity demand increase of 6.6% for the last decade of the 21^st century for low forcing (compared with 1979-2008) without adaptation.

Wan et al (2011b) expanded the research done for Hong Kong to four other major cities in China. They also used a PCA which used the same three climate variables as in Lam et al (2010c) and, generally, the same approach as in Wan et al (2011a). The work done by the authors suggested an increase in the average cooling energy use from 11.4% to 24.2%, dependent on location, for low forcing for the rest of this century and a corresponding decrease in heating of between 13.8% and 26.6%.

The heating energy in the city of Tianjin was investigated by Xiang and Tian (2013) employing a PCA together with a TRNSYS software model of a reference building. Their PCA agreed with Lam et al (2010c) inasmuch as they used the same climate variables.

Based on their PCA and their software model the researchers developed a third order polynomial regression model which predicted a heating energy reduction of 18.1% under low forcing conditions (i.e. under the same conditions as in Lam et al (2010c)) for the latter part of this century compared with the base period from 1971 to 2010. Because the data used to estimate the error was also used for the PCA, the validity of this method may be called into question and, therefore, is not stated here.

Chow et al (2014) investigated how better building regulations may alter the impact of climate change readiness in China. To this end they calculated the energy demand (although no calculation method was given in their paper) of an apartment block in each of four locations covering three climate zones in China. They found that the effect of the new regulations reduced the demand for heating, but pointed out that it depended on the climate

as to whether this was of significance, e.g. if the climate was so warm that heating requirements were already low impact, changes would be small. In addition, they mentioned that these measures may be counterproductive when it came to cooling as they could increase cooling demand.