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Artificial neural network-based pump operation policy models for energy cost minimisation

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dc.contributor.authorZheng, L.
dc.contributor.authorWu, W.
dc.contributor.authorSimpson, A.R.
dc.contributor.authorWang, Y.
dc.date.issued2026
dc.description.abstractEfficient pump operation in water distribution systems (WDSs) is crucial for meeting end-users’ demands while minimising energy costs. Artificial neural network (ANN)-based operation policy models have been developed to address the limitations of existing methods in adjusting pump operations under dynamic conditions, where water demand and electricity tariffs vary. However, developing a suitable ANN-based policy model for a specific WDS is challenging due to the distinctly different learning patterns of ANN architectures and different operational characteristics of fixed and variable speed pumps, which collectively influence model performance. In this study, a modelling framework is proposed that couples a physics-based hydraulic model with an ANN-based operation policy model to inform decision-making under dynamic conditions. Two different ANNs, namely the radial basis function (RBF) and the multilayer perceptron (MLP), have been developed as policy models, and their strengths and limitations were evaluated through two WDSs - one with fixed speed pumps and another with a variable speed pump. The ANN-based policy models have been evaluated for their ability to minimise pumping energy costs and detailed operational behaviour. For fixed speed pumping system, the RBF-based policy model outperforms the MLP-based policy model in minimising energy costs and exhibits more advantageous operational behaviour under conditions driven by peak daily demand. For variable speed pumping system, both ANN-based policy models perform reasonably well in minimising energy costs. The RBF-based policy model remains more advantageous under conditions with peak daily demand, while the MLP-based policy model can be more advantageous under conditions with high electricity tariffs.
dc.description.statementofresponsibilityLang Zheng, Wenyan Wu, Angus R. Simpson, Ye Wang
dc.identifier.citationEngineering Applications of Artificial Intelligence, 2026; 167:113874-1-113874-14
dc.identifier.doi10.1016/j.engappai.2026.113874
dc.identifier.issn0952-1976
dc.identifier.issn1873-6769
dc.identifier.orcidWu, W. [0000-0003-3907-1570]
dc.identifier.orcidSimpson, A.R. [0000-0003-1633-0111]
dc.identifier.urihttps://hdl.handle.net/2440/150066
dc.language.isoen
dc.publisherElsevier
dc.relation.granthttp://purl.org/au-research/grants/arc/DE210100117
dc.relation.granthttp://purl.org/au-research/grants/arc/DE220100609
dc.rights© 2026 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
dc.source.urihttps://doi.org/10.1016/j.engappai.2026.113874
dc.subjectPump operation optimisation Dynamic operational condition Operation policy model Artificial neural network Pumping energy cost
dc.titleArtificial neural network-based pump operation policy models for energy cost minimisation
dc.typeJournal article
pubs.publication-statusPublished

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