The Application of System Dynamics Model of City Water Demand Forecasting

2014 ◽  
Vol 535 ◽  
pp. 440-445
Author(s):  
Sheng Na Yang ◽  
Hua Guo ◽  
Yan Li ◽  
Jun Liang Liu
Keyword(s):  
System Dynamics ◽  
Water Demand ◽  
Demand Forecasting ◽  
Urban Water ◽  
System Dynamics Model ◽  
Dynamics Model ◽  
Demand Prediction ◽  
The Future ◽  
Water Demand Prediction ◽  
City Water

With the continuous development of city, urban water consumption is continuously increasing. But the earth's fresh water resources are limited. If you want to meet the urban development increases the amount of water, you must forecast for urban water demand in the future, water demand prediction is also should be formed. This research mainly adopts the method of system dynamics to predict the water demand of city over the next decade. Forecast of water demand to get accurate effective demand in the future by establishing a system dynamic model, which accuracy should tested and verified.

Construction of System Dynamics Model on Service Strategy and Resource Management

2013 ◽  
Vol 284-287 ◽  
pp. 3662-3666
Author(s):  
Tian Syung Lan ◽  
Chao Hua Kuo ◽  
Chu Ching Liang
Keyword(s):  
Performance Evaluation ◽  
Resource Management ◽  
System Dynamics ◽  
Domain Knowledge ◽  
Evaluation Model ◽  
Demand Forecasting ◽  
Resource Conservation ◽  
System Dynamics Model ◽  
Dynamics Model ◽  
Service Strategy

Competitiveness of a company has been based on the ability of locating and solving the com-mercial troubles more quickly and effectively than other competitors. For small and medium-sized enterprises (SMEs), it is necessary to expand the boundaries of the traditional organizations and promote across-domain knowledge in different industries. Thus, the opportunities of industrial in-novation can be increased, and the bright future of the industry can also be confirmed. In Taiwan, there are numerous SMEs in various industries. Conducting and researching in resource conservation service innovation technology and transforming system are deemed to be important for modern enterprises. This research was aimed at building a service-oriented system for saving the energy and reducing discharges to perform a new mechanism of resource conservation services for SMEs. The project “Construction of System Dynamics Model on Service Strategy and Resource Management” was aimed to assist “Wai Cheong Electrical Engineering Co., Ltd.”at Chung Hsing Industry Park in Miaoli. The System Dynamics was primarily introduced into this project for per-formance analysis, so that the proposed performance evaluation model could be formulated. With the performance evaluation on the indicators, the overall performance structure was clarified as well. After the reliability analysis of the proposed model was verified, the data provided dynamic results on demand forecasting, quality control, and marketing management for decision making. Then, the effectiveness would be practically adjusted for different quality levels to predict the actual need. It’s the essential way to recognize the cost variation and observe other parameters of the marketing strategy. The strategy was effective to achieve the goals of removing the strategic planning con-straints and reducing resource consumption caused by improper decisions. In conclusion, the con-struction of this model would enhance the opportunity to succeed and achieve in the company’s op-timum profits when confronting the current dynamic and competitive market environment.

scholarly journals Water demand forecasting using extreme learning machines

2016 ◽  
Vol 28 (1) ◽  
pp. 37-52 ◽  
Author(s):  
Mukesh Tiwari ◽  
Jan Adamowski ◽  
Kazimierz Adamowski
Keyword(s):  
Water Demand ◽  
Demand Forecasting ◽  
Urban Water ◽  
Demand Model ◽  
Demand Prediction ◽  
Learning Machines ◽  
Water Demand Forecasting ◽  
Urban Water Demand ◽  
Learning Machine ◽  
Similar Accuracy

AbstractThe capacity of recently-developed extreme learning machine (ELM) modelling approaches in forecasting daily urban water demand from limited data, alone or in concert with wavelet analysis (W) or bootstrap (B) methods (i.e., ELM, ELMW, ELMB), was assessed, and compared to that of equivalent traditional artificial neural network-based models (i.e., ANN, ANNW, ANNB). The urban water demand forecasting models were developed using 3-year water demand and climate datasets for the city of Calgary, Alberta, Canada. While the hybrid ELMBand ANNBmodels provided satisfactory 1-day lead-time forecasts of similar accuracy, the ANNWand ELMWmodels provided greater accuracy, with the ELMWmodel outperforming the ANNWmodel. Significant improvement in peak urban water demand prediction was only achieved with the ELMWmodel. The superiority of the ELMWmodel over both the ANNWor ANNBmodels demonstrated the significant role of wavelet transformation in improving the overall performance of the urban water demand model.

Water Demand Forecasting in Hubei Province with BP Neural Network Model

2012 ◽  
Vol 599 ◽  
pp. 701-704
Author(s):  
Zhen Quan Tang ◽  
Gang Liu ◽  
Wen Nian Xu ◽  
Zhen Yao Xia ◽  
Hai Xiao
Keyword(s):  
Neural Network ◽  
Water Resources ◽  
Bp Neural Network ◽  
Water Demand ◽  
Demand Forecasting ◽  
Hubei Province ◽  
Demand Prediction ◽  
Water Demand Prediction ◽  
Basic Link ◽  
Bp Neural Network Model

Prediction of water demand is a basic link in water resources plan and management. Reasonable and accurate prediction of storage helps to develop the plan of water resources the next year, which is very favorable to improve the utilization ratio of water resources and reduce the waste of water resources. This paper uses BP neural network to simulate and predict the water content based on the data of water in recent ten years in Hubei province and evaluates the forecast results. The results show that BP neural network for water demand prediction is feasible.

Application of Urban Water Demand Prediction Model by Using Particle Swarm Algorithm Based on Simulated Annealing

2012 ◽  
Vol 155-156 ◽  
pp. 102-106
Author(s):  
Bo Sun ◽  
Jian Cang Xie ◽  
Ni Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Water Demand ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Urban Water ◽  
Swarm Optimization ◽  
Demand Prediction ◽  
Water Demand Prediction

Water demand prediction is a complicated multifactor, multi-level non-linear system influenced by the urban population, industrial and economic level. The results of the prediction accuracy have a greater uncertainty and ambiguity. As a new cluster intelligent evolutionary algorithms, particle swarm optimization (PSO) is easy to understand, easy to implement ,and it is very suitable for non-linear model parameters fitting problems. At the same time, we will introduce the simulated annealing mechanism into particle swarm optimization algorithm, constructed the optimization algorithm of simulated annealing particle swarm (SA-PSO). In the paper, the optimization algorithm of simulated annealing particle swarm (SA-PSO) is applied to the field of water demand prediction. Example show that compared with the particle swarm algorithm, simulated annealing particle swarm optimization achieves a high prediction accuracy for urban water demand prediction, and it is strong applicability in the water demand forecast.

scholarly journals China's Copper Demand Forecasting Based on System Dynamics Model

2017 ◽  
Author(s):  
Jianbo Yang ◽  
Xin Li ◽  
Qunyi Liu
Keyword(s):  
System Dynamics ◽  
Moving Average ◽  
Social Indicators ◽  
Demand Forecasting ◽  
System Dynamics Model ◽  
Dynamics Model ◽  
Vector Autoregressive ◽  
Resource Demand ◽  
Demand Growth ◽  
Autoregressive Moving Average Model

Copper demand for a country's copper industry has a greater pull effect. China's copper consumption in 2015 has accounted for 50% of the world. The scientific forecast of China's copper demands trend is also an important basis for analyzing its future environmental impact. This paper assumes that China's economy will be developing high, medium and low scenarios, and forecasts economic and social indicators such as total GDP, population and per capita GDP in China from 2016 to 2030. Then, predicted the demand of copper resources in China from 2016 to 2030 by the combination of system dynamics model, vector autoregressive moving average model and inverted U-type empirical model. The results show that: (1) in 2020, 2025 and 2030, China's refined copper demand will be 13 Mt, 15 Mt and 15.5 Mt. (2) China's copper demand growth slowed down significantly from 2016-2030. (3) 2025-2030, China's copper resource demand is stable, into the platform of demand growth, the highest peak value in 2027 will be 15.5 Mt. (4) 2030 years later, China's copper resource demand will enter a slow decline.

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