A methodology for the robust design of an ambient-air vaporizer under time-series weather conditions is proposed. Two techniques are used to extract representative features in the time-series data. (i) The major trend of a day is rapidly identified by the discrete wavelet transform (DWT), in which a high level of Haar function reflects the trend of a day and drastically reduces the data size. (ii) The k-means clustering method groups the similar features of a year, and the reconstructed time-series dataset extracted by the centroids of clusters represents the weather conditions of a year. The results of the multi-feature-based optimization were compared with non-wavelet based and multi-period optimization by simulation under a year of data. The design structure from the feature extraction shows 22.92% better performance than the original case and is 12 times more robust in different weather conditions than clustering with raw data.

提出了一种在时序天气条件下稳健设计环境空气蒸发器的方法。使用两种技术来提取时间序列数据中的代表性特征。（i）一天的主要趋势可以通过离散小波变换（DWT）快速识别，其中高水平的Haar函数反映了一天的趋势，并极大地减少了数据量。（ii）k-均值聚类方法将一年的相似特征分组，并且由聚类的质心提取的重建的时间序列数据集表示一年的天气状况。通过一年的数据模拟，将基于多特征的优化结果与基于非小波和多周期的优化结果进行了比较。从特征提取中提取的设计结构比原始情况显示出22.92％的性能，并且在不同天气条件下的健壮性是原始数据聚类的12倍。