Unlocking the Potential of Wastewater Treatment: Machine Learning Based Energy Consumption Prediction

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Wastewater treatment plants (WWTPs) are energy-intensive facilities that fulfill stringent effluent quality norms. Energy consumption prediction in WWTPs is crucial for cost savings, process optimization, compliance with regulations, and reducing the carbon footprint. This paper evaluates and compares a set of 23 candidate machine-learning models to predict WWTP energy consumption using actual data from the Melbourne WWTP. To this end, Bayesian optimization has been applied to calibrate the investigated machine learning models. Random Forest and XGBoost (eXtreme Gradient Boosting) were applied to assess how the incorporated features influenced the energy consumption prediction. In addition, this study investigated the consideration of information from past data in improving prediction accuracy by incorporating time-lagged measurements. Results showed that the dynamic models using time-lagged data outperformed the static and reduced machine learning models. The study shows that including lagged measurements in the model improves prediction accuracy, and the results indicate that the dynamic K-nearest neighbors model dominates state-of-the-art methods by reaching promising energy consumption predictions.
Original languageEnglish (US)
Pages (from-to)2349
Issue number13
StatePublished - Jun 25 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-07-12
Acknowledged KAUST grant number(s): ORA-2022-5339
Acknowledgements: This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST) Research Funding (KRF) from the Climate and Livability Initiative (CLI) under Award No. ORA-2022-5339.

ASJC Scopus subject areas

  • Water Science and Technology
  • Biochemistry
  • Aquatic Science
  • Geography, Planning and Development


Dive into the research topics of 'Unlocking the Potential of Wastewater Treatment: Machine Learning Based Energy Consumption Prediction'. Together they form a unique fingerprint.

Cite this