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Open AccessFeature PaperArticle

A Novel Dual-Scale Deep Belief Network Method for Daily Urban Water Demand Forecasting

1
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
2
Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, College of Physics and Electronic Engineering, Hengyang Normal University, Hengyang 421002, China
*
Author to whom correspondence should be addressed.
Energies 2018, 11(5), 1068; https://doi.org/10.3390/en11051068
Received: 6 April 2018 / Revised: 22 April 2018 / Accepted: 23 April 2018 / Published: 26 April 2018
(This article belongs to the Section Energy Storage and Application)
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 using generalized Fourier transform. The deep belief network (DBN) model was used for prediction using the two feature terms. The outputs of the double DBNs are summed as the final forecasting results. Historical daily water demand datasets from an urban waterworks in Zhuzhou, China, were investigated by the proposed DSDBN model. The mean absolute percentage error (MAPE), normalized root-mean-square error (NRMSE), correlation coefficient (CC) and determination coefficient (DC) were used as evaluation criteria. The results were compared with the autoregressive integrated moving average (ARIMA) model, feed forward neural network (FFNN) model, support vector regression (SVR) model, EEMD and their combinations, and single DBN model. The results obtained in the test period indicate that the proposed model has the smallest MAPE and NRMSE values of 1.291099 and 0.016625, respectively, and the largest CC and DC values of 0.976528 and 0.953512, respectively. Therefore, the proposed DSDBN method is a useful tool for daily urban water demand forecasting and outperforms other models in common use. View Full-Text
Keywords: daily water demand forecasting; ensemble empirical mode decomposition; deep belief network; dual-scale daily water demand forecasting; ensemble empirical mode decomposition; deep belief network; dual-scale
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MDPI and ACS Style

Xu, Y.; Zhang, J.; Long, Z.; Chen, Y. A Novel Dual-Scale Deep Belief Network Method for Daily Urban Water Demand Forecasting. Energies 2018, 11, 1068.

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