期刊论文

Xu, Yuebing

英文

Energies

1996-1073

2018

11

5

1068

10.3390/en11051068

This work was supported in part by the National Natural Science Foundation of China (61573299), the Science and Technology Plan Project of Hunan Province (2016TP1020), the Open fund project of Hunan Provincial Key Laboratory of Intelligent Information Processing and Application for Hengyang normal university (2017IIPAYB04), the Natural Science Foundation of Hunan Province (2017JJ2011), and the Research Project of the Education Department of Hunan Province (17A031).

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Water demand forecasting applies data supports for the scheduling and decision-making of urban water supply systems. In this study, a new dual-scale deep belief network (DSDBN) approach for daily urban water demand forecasting was proposed. Original daily water demand time series was decomposed into several intrinsic mode functions (IMFs) and one residue component with ensemble empirical mode decomposition (EEMD) technique. Stochastic and deterministic terms were reconstructed through analyzing the frequency characteristics of IMFs and residue ...