## 6 Discussion  
Considering the influence of climate change, an appropriate meteorological statistical period (15 years) was selected, and based on the hourly data and daily mean data of dry bulb temperature, wet bulb temperature, and wind speed in Beijing and Shanghai over 15 years, the hourly variation coefficient \(\beta\) of dry bulb temperature in \"Design Code for Heating, Ventilation and Air Regulation of Civil Buildings\" GB 50736-2012 was modified. Through the Pearson correlation analysis and the CRITIC weight, the coupling between different meteorological elements was considered. The K-means clustering method was adopted to classify \(\beta\), and the \(\beta\) of the class with the largest proportion was selected to calculate the mean value. The \(\beta\) meeting the local meteorological conditions was generated, and the LSTM neural network was used to predict the change in \(\beta\) in a future period. At the same time, the calculated temperature of summer air conditioning and the calculated daily average temperature of summer air conditioning were re-selected to generate the revised local design days. Then, the average urban heat island intensity in different regions (Beijing selected the Chaoyang area, Shanghai selected the Jiading area) was used to calculate the design days of Chaoyang and Jiading under the influence of the urban heat island effect and the corresponding \(\beta\). Finally, taking the hospital building as an example, DeST software was used to model the outpatient building and inpatient building, and the changes in hourly cooling load and hourly cooling load per unit area on the modified design day were analyzed under the influence of the urban heat island effect. Through the analysis of the summer cooling load of different functional rooms, the characteristics of the hospital building in summer cooling load were reflected. It can be seen from the experimental results that, compared with before the correction, the hourly cooling load of the unit area of the hospital building changes significantly in summer:  
1. Compared with the design day in the specification, the revised design day is more in line with the actual local meteorological conditions and has obvious regional differences. The maximum temperature in Beijing was one hour later than the norm and appeared at 15:00, with a temperature rise of about 2 \({}^{\circ}\)C. In the future, the temperature is expected to stabilize at 15:00, with the temperature falling back to the normal value. In Shanghai, the maximum temperature of the whole day was 1 h later than the norm and appeared at 15:00, with a temperature rise of about 4 \({}^{\circ}\)C. In the future, it is expected to continue to be 1 h to 16:00, with a temperature rise of about 6 \({}^{\circ}\)C based on the normal value. 2. The influence time and intensity of the heat island effect on the summer cooling load in different cities are different. The load in the Chaoyang District of Beijing increased significantly from 15:00 to 7:00 am of the next day in summer, and the load from 8:00  
Figure 19: Statistics of summer cooling load in Beijing (**a**) and Shanghai (**b**) inpatient buildings. to 14:00 was roughly the same as before the correction. The standard load ratio of outpatient buildings in hospitals will decrease by 0.69%, increase by 12.12% under the influence of the heat island effect, and decrease by 1.35% in the future; the standard load ratio of inpatient buildings will increase by 0.27%, increase by 7.13% under the influence of the heat island effect, and decrease by 0.93% in the future. It shows that climate change and the heat island effect have an obvious influence on the increase in the air conditioning cooling load in Beijing in the short term, but in the long term, the cooling load will fall back to the previous level. In the Jiading District of Shanghai, the load increased significantly from 17:00 to 6:00 in the morning of the next day, and the load from 7:00 to 16:00 was roughly the same as before the correction.