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From prediction to sustainability: AI for smart energy management in wastewater treatment plants
Alsamhi, Saeed Hamood Hawbani, Ammar Al-qaness, Mohammed A.A. O’brolchain, Niall Zhao, Liang Al-Dubai, Ahmed Yassin Asghar, Mamoona Naveed Algabri, Redhwan Guizani, Mohsen
Alsamhi, Saeed Hamood
Hawbani, Ammar
Al-qaness, Mohammed A.A.
O’brolchain, Niall
Zhao, Liang
Al-Dubai, Ahmed Yassin
Asghar, Mamoona Naveed
Algabri, Redhwan
Guizani, Mohsen
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s41598-025-28281-2.pdf
Adobe PDF, 3.74 MB
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Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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Abstract
In wastewater treatment plants (WWTPs), accurate energy forecasting is crucial for optimizing operations, promoting self- sufficiency, and ensuring sustainability. We compare and evaluate the performance of Machine Learning (ML) techniques for energy self-consumption (i.e., long-term memory (LSTM), support vector machines (SVM), recurring neural networks (RNN), gated recurrent units (GRU), and XGBoost), to forecast energy generation (EG) and energy consumption (EC) in WWTPs. The performance of models is evaluated using metrics (i.e., mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE)), based on a solid dataset of daily operating records. The findings show that GRU achieves the highest performance, with an RMSE of 0.102, MAE of 0.085, and R² of 0.978, followed by LSTM, GRU, and RNN. The models demonstrate temporal prediction capabilities, as well as driving energy efficiency and reducing operational costs in WWTPs. This paper offers insights into implementing ML for sustainable energy management in WWTPs to energy forecasting, enhancing energy self-consumption, and boosting operational efficiency and environmental sustainability.
Citation
S. H. Alsamhi et al., “From prediction to sustainability: AI for smart energy management in wastewater treatment plants,” Scientific Reports 2025 15:1, vol. 15, no. 1, pp. 44598-, Dec. 2025, doi: 10.1038/s41598-025-28281-2.
Source
Scientific Reports
Conference
Keywords
Energy consumption, Energy generation, Machine learning, Self-consumption, Smart energy, Sustainability, Transforming energy forecasting, Wastewater treatment plants
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Publisher
Springer Nature