From Prediction to Sustainability: AI for Smart Energy Management in Wastewater Treatment Plants

From Prediction to Sustainability: AI for Smart Energy Management in Wastewater Treatment Plants | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article From Prediction to Sustainability: AI for Smart Energy Management in Wastewater Treatment Plants Saeed Hamood Alsamhi, Ammar Hawbani, Niall O’Brolchain, Mohammed A. A. Al-qaness, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7274115/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Accurate energy prediction is vital in optimizing operations and self-consumption and ensuring sustainability goals in wastewater treatment plants (WWTPs) and environmental applications. Self- consumption refers to the proportion of energy produced locally that is used on-site to meet energy demands. To anticipate energy generation and consumption, this paper compares and evaluates the performance of Machine Learning (ML) techniques for energy self-consumption, including long- term memory (LSTM), support vector machines (SVM), recurring neural networks (RNN), gated recurrent unit (GRU), and XGBoost, to forecast energy generation (EG) and energy consumption (EC) in WWTPs. The performance of each model is evaluated using metrics such as mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) using a solid dataset of daily operating records. The findings show that GRU achieves the highest performance with RMSE of 0.102, MAE of 0.085, and R² of 0.978, followed by LSTM, GRU, and RNN, showcasing reliable temporal prediction capabilities, as well as these models driving energy efficiency and reducing operational costs in WWTPs. This paper highlights actionable insight into adopting ML for sustainable energy management in WWTPs, transforming energy forecasting, improving energy self-consumption, and establishing the framework for increasing WWTP’s operational efficiency and environmental sustainability. Physical sciences/Energy science and technology Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing Energy generation Energy consumption Self-consumption Wastewater treatment plants Sustainability transforming energy forecasting Deep learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 13 Oct, 2025 Reviews received at journal 10 Oct, 2025 Reviews received at journal 06 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers agreed at journal 05 Oct, 2025 Reviewers agreed at journal 04 Oct, 2025 Reviewers agreed at journal 04 Oct, 2025 Reviewers invited by journal 02 Sep, 2025 Editor assigned by journal 02 Sep, 2025 Editor invited by journal 20 Aug, 2025 Submission checks completed at journal 13 Aug, 2025 First submitted to journal 13 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7274115","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":511473957,"identity":"659e4507-0035-4d3a-8e14-501edc82e2be","order_by":0,"name":"Saeed Hamood Alsamhi","email":"data:image/png;base64,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","orcid":"","institution":"University of Galway","correspondingAuthor":true,"prefix":"","firstName":"Saeed","middleName":"Hamood","lastName":"Alsamhi","suffix":""},{"id":511473958,"identity":"4fc29d6a-b6d4-4a9d-b051-2601b805575f","order_by":1,"name":"Ammar Hawbani","email":"","orcid":"","institution":"IBB University","correspondingAuthor":false,"prefix":"","firstName":"Ammar","middleName":"","lastName":"Hawbani","suffix":""},{"id":511473959,"identity":"24ca2b1d-653c-4374-b202-83fc40e88c3e","order_by":2,"name":"Niall O’Brolchain","email":"","orcid":"","institution":"University of Galway","correspondingAuthor":false,"prefix":"","firstName":"Niall","middleName":"","lastName":"O’Brolchain","suffix":""},{"id":511473960,"identity":"1b7240a6-d070-49fd-9d2c-923a77d4c01d","order_by":3,"name":"Mohammed A. 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