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.
Highlights
Short-term water demand prediction is the basis for optimal operation scheduling and decision-making of urban water supply systems, potentially providing a guide for the optimal operation scheduling of pumping stations, reduced energy consumption of water production and decreased economical costs of water supply
Wang et al [30] implemented the artificial neural network (ANN) model based on ensemble empirical mode decomposition (EEMD) to forecast medium and long-term runoff, the results indicate that EEMD can markedly improve forecasting accuracy
This paper proposes a novel dual-scale deep belief network (DSDBN) method for daily urban water demand forecasting
Summary
Short-term water demand prediction is the basis for optimal operation scheduling and decision-making of urban water supply systems, potentially providing a guide for the optimal operation scheduling of pumping stations, reduced energy consumption of water production and decreased economical costs of water supply. Domestic water cannot be stored for extended periods, balanced water production and supply can be achieved by accurate short-term water demand prediction, and the quality of water supply can be guaranteed. The two main approaches to water demand forecasting are knowledge-driven modeling and data-driven modeling. The former can contain detailed description of factors that affect urban water demand, such as population, price, income, Energies 2018, 11, 1068; doi:10.3390/en11051068 www.mdpi.com/journal/energies
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