Abstract
The air humidity is one essential measurable characteristic widely used in engineering and science. It is highly associated with the air temperature and closely involved with our daily life and work in indoor or outdoor environments. As high humidity can cause damage to building materials and resident health, the humidity issue in buildings seems to have been one widespread concern recently. There is a growing recognition that humidity control is crucial for various occasions. Changes of daily 24-h air humidity, which may be influenced by many factors, can also be expressed as one time series. Contributions of this study are: Firstly, for engineering application purposes, one concise and well-formed called the LS (“least-squares”)-extended DCT (“discrete cosine transform”) model is proposed for hourly air humidity forecasting from the perspective of time-series analyzing or data-driven modeling. Actually, conventional Fourier-based models cannot be straightly applied for forecasting. To reckon the LS-optimal DCT coefficients for forecast modeling on the basis of limited humidity time-series observations, the LS approach is integrated in implementation of the proposed LS-extended DCT forecast model. Then, the proposed method is applied to tasks of forecasting hourly air humidity changes at eight observing stations. Computer-based modeling and simulation results of hourly air humidity prediction at the observing stations as well as comprehensive comparisons indicate potentiality of the proposed method that it provides one concise and well-formed approach for hourly humidity modeling and prediction. To conclude, the proposed method can be used for reference to other related engineering applications.
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Acknowledgements
The research was supported by “Scientific Research Fund of Hunan Provincial Science and Technology Department, China” (2013GK3090).
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The research was supported by “Scientific Research Fund of Hunan Provincial Science and Technology Department, China” (2013GK3090).
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Yang, Zc. Data-driven discrete cosine transform (DCT)-based modeling and simulation for hourly air humidity prediction. Soft Comput 28, 541–563 (2024). https://doi.org/10.1007/s00500-023-08297-4
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DOI: https://doi.org/10.1007/s00500-023-08297-4