Hybrid deterministic and machine learning approach for smart energy audits in wastewater treatment plants

Abstract Energy audits in wastewater treatment plants (WWTPs), conducted under ISO 50002 and aligned with ISO 50001, are essential to improve sustainability in a highly energy-intensive sector. However, the nonlinearity of biological processes, operational variability, and the limited number of conventional indicators hinder the accurate identification and interpretation of energy inefficiencies. This study proposes SEA-WWTPs, a hybrid quantitative methodology combining deterministic diagnostics with machine learning models trained on event-level features derived from monitored energy performance indicators (EnPIs), to detect and classify energy-inefficiency events and rank likely root causes to support audit-oriented corrective decision-making. A case study in a leachate wastewater treatment plant (WWTP-L) in Portugal analyzed 484,810 1-min records collected over approximately three years. A total of 28,608 inefficiency occurrences (aggregated events) were detected; 96.25% were persistent, indicating predominance of structural and electromechanical causes. The random forest model achieved a Macro-F1 of 0.716 and a Top-2 accuracy of 0.788 using equipment-grouped GroupKFold validation. Overall, the proposed methodology enhances energy audits by integrating data-driven decision support, ensuring traceability, audit-oriented interpretability, and compatibility with ISO standards, thereby supporting the digitalization of sustainable energy management in WWTPs and contributing to the sustainable development goals (SDGs).