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<p>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.</p>

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From prediction to sustainability: AI for smart energy management in wastewater treatment plants

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月16日

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  • Saeed Hamood Alsamhi,
  • Ammar Hawbani,
  • Mohammed A. A. Al-qaness,
  • Niall O’Brolchain,
  • Liang Zhao,
  • Ahmed Al-Dubai,
  • Mamoona Asghar,
  • Redhwan Algabri,
  • Mohsen Guizani

摘要

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.