## 1 Introduction
determined the minimum statistical duration of outdoor dry bulb temperature calculation and the best mean value of meteorological elements according to the standard deviation method and the optimal climate normal (OCN) model and selected the statistical duration of outdoor dry bulb temperature calculation through comparison. Taking Tianjin as an example, the statistical duration of dry bulb temperature, calculated using the ASHRAE method and China method, is obtained [47]. At present, the selection of meteorological parameters in the field of air conditioning in China has some problems, such as incomplete parameter types and imperfect statistical methods [24]. Countries around the world began revising standards for design days in the 1930s. The current standard of ASHRAE in the United States considers the law of simultaneous occurrence of meteorological parameters, but using the average value of corresponding parameters as the combination index cannot accurately reflect the probability of occurrence of outdoor air conditioning points. Similar to the United States, the United Kingdom and Japan use the non-guaranteed rate form and do not classify by use. Since 1975, the selection of parameters has not changed substantially. In the case of simultaneous occurrence of meteorological parameters, the current method of selecting a single point value calculated according to the dry bulb temperature of outdoor air conditioning does not consider the hourly variation in wet bulb temperature and wind speed in the calculation of air conditioning load. ASHRAE uses simultaneous averaging of a single parameter and another parameter, and only considers partial simultaneity. All outdoor parameters occur at the same time. If each parameter is analyzed separately, the characteristics of parameter simultaneity will be ignored. Therefore, the simultaneous occurrence of humidity and wind speed should be considered in the selection of dry bulb temperature data, so as to ensure the accuracy and rationality of outdoor meteorological parameters. The rapid development of meteorological data and the lack of updating of research methods, coupled with the impact of climate change after the 1970s, have gradually derailed research results and engineering practice and gradually derailed international research. Therefore, in consideration of parameter coupling and data updates, the previous design day parameter selection method needs to be improved to meet the actual requirements of scientific research and engineering applications [48]. In China's \"13 th Five-Year Plan\", building energy efficiency received more attention [38]. The proposal of a double carbon target further clarifies the development direction of the research on building energy consumption. With the deepening of a large number of related studies, meteorological data have been greatly supplemented and improved both in time and space, and their accuracy has been continuously improved. At the same time, the rapid development of artificial intelligence algorithms enables us to have more rapid and reliable means to study problems in the face of massive data, which provides the basis for the fine study of relevant parameters of air conditioning design. The Recurrent NeuralNetwork (RNN) [49] in machine learning algorithms is widely used to deal with time-series prediction because it has a recurrent neural network (RNN) that can better process sequential information, that is, the influence of the input between the front and back. Among them, Long Short-Term Memory network (LSTM), as a special RNN, can solve the common problem of long-term dependence in general recursive neural networks and has better performance in longer time series. Compared with other artificial intelligence algorithms, it is more suitable to study the selection of relevant meteorological parameters under climate change because it can retain the long-term dependence relationship caused by time characteristics to a greater extent [50].