An ensemble stacked model with bias correction for improved water demand forecasting

ABSTRACT

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.

KEYWORDS:

• Water demand forecasting
• gradient boosting machines
• machine learning
• model stacking
• bias correction
• deep neural networks

Acknowledgements

This study was funded as part of the Water Informatics Science and Engineering Centre for Doctoral Training (WISE CDT) under a grant from the Engineering and Physical Sciences Research Council (EPSRC), grant number EP/L016214/1. The authors would like to thank Wessex Water and Chris Hutton for providing the data for this study. The code used for the bias correction was adjusted from the paper of Song (2015).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Engineering and Physical Sciences Research Council [EP/L016214/1].