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

Yuebing Xu, Jing Zhang, Zuqiang Long and Yan Chen
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Yuebing Xu: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Jing Zhang: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Zuqiang Long: Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, College of Physics and Electronic Engineering, Hengyang Normal University, Hengyang 421002, China
Yan Chen: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China

Energies, 2018, vol. 11, issue 5, 1-15

Abstract: 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.

Keywords: daily water demand forecasting; ensemble empirical mode decomposition; deep belief network; dual-scale (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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