Print Email Facebook Twitter An ensemble stacked model with bias correction for improved water demand forecasting Title An ensemble stacked model with bias correction for improved water demand forecasting Author Xenochristou, Maria (University of Exeter) Kapelan, Z. (TU Delft Sanitary Engineering; University of Exeter) Date 2020 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. Subject bias correctiondeep neural networksgradient boosting machinesmachine learningmodel stackingWater demand forecasting To reference this document use: http://resolver.tudelft.nl/uuid:72b4d40d-cfd1-47a2-80b7-c2b8b3628fae DOI https://doi.org/10.1080/1573062X.2020.1758164 Embargo date 2021-05-13 ISSN 1573-062X Source Urban Water Journal, 17 (3), 212-223 Bibliographical note Accepted Author Manuscript Part of collection Institutional Repository Document type journal article Rights © 2020 Maria Xenochristou, Z. Kapelan Files PDF Xenochristou_and_Kapelan_ ... 20_UWJ.pdf 1.78 MB Close viewer /islandora/object/uuid:72b4d40d-cfd1-47a2-80b7-c2b8b3628fae/datastream/OBJ/view