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

Abstract

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.

Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors

“…Although ANN has a higher level of accuracy, MLR models are simple and understandable for non-professionals [61]. Moreover, ANN has been found to be more accurate than Linear regression, support vector machine [62] and, MARS [63] methodologies for modeling nonlinear fluctuating phenomena [64]. ANN is capable of modeling energy consumption based on hourly intervals [65] or even less than hourly intervals (e.g., 15 min) [66].…”

Analysis and Forecasting of the Energy Consumption in Wastewater Treatment Plant

“…Neural networks played an important role in solving these problems. The model has been widely applied to various fields of mathematics, engineering, economics [22,33,34]. It can model any nonlinear system to a high degree of accuracy by adjusting the network parameters and uses the steepest descent method to search for the optimal solution.…”

Forecasting of Wastewater Discharge and the Energy Consumption in China Based on Grey Model

“…Chen et al investigated the overall status of wastewater discharge in China to analyze its driving factors [12]. Yin et al developed a neural network model to forecast and analyze urban water-energy demand in China [13]. Xie and Wang examined the challenge of energy consumption in wastewater treatment plants [14].…”