Multivariate Regression Models for Predicting Pump-as-Turbine Characteristics

Abstract

Installing pumps as turbines (PaTs) in water distribution networks can recover otherwise wasted energy, as well as reduce leakage caused by high water pressure. However, a barrier to their implementation is the lack of information on their performance in turbine mode. Previous studies have proposed models to predict PaT characteristics based on pump best efficiency points (BEPs), using regressions with one or two dependent variables, or more complex artificial neural networks (ANNs). While ANNs were found to improve the accuracy of predictions, these models are known to be unstable with small datasets. Other types of regressions with multiple variables have not been explored. Furthermore, because only small datasets are available to train these models, multivariate regression methods could yield better results. The present study develops multivariate regression models to predict BEPs and characteristic curves of PaTs. A database of 145 BEPs and 196 characteristic curve PaT experimental records was compiled from previous literature. Twenty-four types of multi-variate regressions, as well as ANN were compared, with dimensioned and dimensionless versions of the datasets. The multivariate regression models consistently outperformed previous models, including ANN. The R2 of the head and efficiency curves were 0.997 and 0.909, respectively. Results also showed that XGB regressors and a dimensionless dataset yielded the best-fit models overall. The high accuracy of the models, combined with their lower computational cost compared to ANN, make them a robust solution for selecting PaTs in practice.