Short-Term Urban Water Demand Prediction Considering Weather Factors

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.

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ELM based short-term Water Demand Prediction for Effective Operation of Water Treatment Plant

Journal of the Korean Institute of Illuminating and Electrical Installation Engineers ◽

10.5207/jieie.2009.23.9.108 ◽

2009 ◽

Vol 23 (9) ◽

pp. 108-116 ◽

Cited By ~ 2

Keyword(s):

Water Treatment ◽

Water Demand ◽

Treatment Plant ◽

Water Treatment Plant ◽

Short Term ◽

Demand Prediction ◽

Effective Operation ◽

Water Demand Prediction

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.155-156.102 ◽

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.

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