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• W2885664578 abstract "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." @default.
• W2885664578 created "2018-08-22" @default.
• W2885664578 creator A5004374498 @default.
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• W2885664578 date "2018-10-01" @default.
• W2885664578 modified "2023-10-18" @default.
• W2885664578 title "Robust design of ambient-air vaporizer based on time-series clustering" @default.
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• W2885664578 doi "https://doi.org/10.1016/j.compchemeng.2018.08.026" @default.
• W2885664578 hasPublicationYear "2018" @default.
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