Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
An ensemble stacked model with bias correction for improved water demand forecasting
Water demand forecasting is an essential task for water utilities, with increasing importance due to future societal and environmental changes. This paper suggests a new methodology for water demand forecasting, based on model stacking and bias correction that predicts daily demands for groups of ~120 properties. This methodology is compared to a number of models (Artificial Neural Networks – ANNs, Generalised Linear Models – GLMs, Random Forests – RFs, Gradient Boosting Machines – GBMs, Extreme Gradient Boosting – XGBoost, and Deep Neural Networks – DNNs), using real consumption data from the UK, collected at 15–30 minute intervals from 1,793 properties. Results show that the newly proposed stacked model that comprises of RFs, GBMs, DNNs, and GLMs consistently outperformed other water demand forecasting techniques (peak R2 = 74.1%). The stacked model’s accuracy on peak consumption days further improved by applying a bias correction method on the model’s output.
An ensemble stacked model with bias correction for improved water demand forecasting
Water demand forecasting is an essential task for water utilities, with increasing importance due to future societal and environmental changes. This paper suggests a new methodology for water demand forecasting, based on model stacking and bias correction that predicts daily demands for groups of ~120 properties. This methodology is compared to a number of models (Artificial Neural Networks – ANNs, Generalised Linear Models – GLMs, Random Forests – RFs, Gradient Boosting Machines – GBMs, Extreme Gradient Boosting – XGBoost, and Deep Neural Networks – DNNs), using real consumption data from the UK, collected at 15–30 minute intervals from 1,793 properties. Results show that the newly proposed stacked model that comprises of RFs, GBMs, DNNs, and GLMs consistently outperformed other water demand forecasting techniques (peak R2 = 74.1%). The stacked model’s accuracy on peak consumption days further improved by applying a bias correction method on the model’s output.
An ensemble stacked model with bias correction for improved water demand forecasting
Xenochristou, Maria (Autor:in) / Kapelan, Zoran (Autor:in)
Urban Water Journal ; 17 ; 212-223
15.03.2020
12 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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