The efficiency of wastewater treatment systems must be reflected in the removal of the pollutant load from the influent and the optimal energy performance of electrical equipment. Wastewater Treatment Plants (WWTPs) are part of the Intensive Energy Consumption Management System (SGCIE) and are therefore subject to mandatory energy audits. This article aims to assess the impact of an energy audit in a WWTP, according to ISO 50001:2018 and the Plan-Do-Check-Act (PDCA) methodology, to identify and quantify both persistent and transient energy inefficiencies. According to the results, the energy audit contributed to an approximate 10.8% reduction in electrical energy consumption. During the assessment, several challenges were identified that may compromise the effectiveness of audits in improving energy performance. The complexity of the treatment model, aging infrastructure and equipment, the lack of real-time data, and a limited number of indicators hinder the proper management of inefficiency phenomena, particularly transient ones.

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).