Short-Term Water Demand Forecast Based on Deep Learning Method

AbstractShort-time water demand forecasting is essential for optimal control in a water distribution system (WDS). Current methods (e.g., time-series models and conventional artificial neural networks) have limited power in practice due to the nonlinear nature of changes in water demand. In particul...

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Veröffentlicht in:	Journal of water resources planning and management 2018-12, Vol.144 (12)
Hauptverfasser:	Guo, Guancheng, Liu, Shuming, Wu, Yipeng, Li, Junyu, Zhou, Ren, Zhu, Xiaoyun
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Autoregressive models Current distribution Deep learning Demand Economic forecasting Error correction Forecasting Learning Learning theory Lifting tackle Machine learning Neural networks Optimal control Performance enhancement Solutions Supply-demand forecasting Teaching methods Technical Papers Water Water demand Water distribution Water distribution systems Water engineering Water resources management
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