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Open AccessArticle

Comprehensive Forecast of Urban Water-Energy Demand Based on a Neural Network Model

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State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
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The College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
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Author to whom correspondence should be addressed.
Water 2018, 10(4), 385; https://doi.org/10.3390/w10040385
Received: 25 January 2018 / Revised: 18 March 2018 / Accepted: 21 March 2018 / Published: 26 March 2018
(This article belongs to the Special Issue Water Quality: A Component of the Water-Energy-Food Nexus)
Water-energy nexus has been a popular topic of rese arch in recent years. The relationships between the demand for water resources and energy are intense and closely connected in urban areas. The primary, secondary, and tertiary industry gross domestic product (GDP), the total population, the urban population, annual precipitation, agricultural and industrial water consumption, tap water supply, the total discharge of industrial wastewater, the daily sewage treatment capacity, total and domestic electricity consumption, and the consumption of coal in industrial enterprises above the designed size were chosen as input indicators. A feedforward artificial neural network model (ANN) based on a back-propagation algorithm with two hidden layers was constructed to combine urban water resources with energy demand. This model used historical data from 1991 to 2016 from Wuxi City, eastern China. Furthermore, a multiple linear regression model (MLR) was introduced for comparison with the ANN. The results show the following: (a) The mean relative error values of the forecast and historical urban water-energy demands are 1.58 % and 2.71%, respectively; (b) The predicted water-energy demand value for 2020 is 4.843 billion cubic meters and 47.561 million tons of standard coal equivalent; (c) The predicted water-energy demand value in the year 2030 is 5.887 billion cubic meters and 60.355 million tons of standard coal equivalent; (d) Compared with the MLR, the ANN performed better in fitting training data, which achieved a more satisfactory accuracy and may provide a reference for urban water-energy supply planning decisions. View Full-Text
Keywords: water-energy nexus; water demand forecast; energy demand forecast; artificial neural network model; multiple linear regression water-energy nexus; water demand forecast; energy demand forecast; artificial neural network model; multiple linear regression
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Yin, Z.; Jia, B.; Wu, S.; Dai, J.; Tang, D. Comprehensive Forecast of Urban Water-Energy Demand Based on a Neural Network Model. Water 2018, 10, 385.

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