Treatment of municipal wastewater to meet the stringent effluent quality standards is an energy-intensive process and the main contributor to the costs of wastewater treatment plants (WWTPs). Analysis and prediction of energy consumption (EC) are essential in designing and operating sustainable energy-saving WWTPs. In this study, the effect of wastewater, hydraulic, and climate-based parameters on the daily consumption of EC by East Melbourne WWTP was investigated based on the data collected over six years (2014-2019). Data engineering methods were applied to combine features from different resources. To this end, four various feature selection (FS) algorithms were used to reveal the relations among those variables and to select the most relevant variables for training the machine learning (ML) models. Further, the application of artificial neural networks (ANN) and two decision tree algorithms of Gradient Boosting Machine (GBM), and Random Forest (RF) were studied to predict EC records followed by a 95% confidence interval assessment. Results of FS algorithms revealed that total nitrogen, chemical oxygen demand (COD), and inflow-flow had the highest impact on WWTP energy consumption. Moreover, GBM had the best performance prediction among all other regression algorithms. 95% of confidence interval showed a reasonable prediction error band ($\pm 68MWh/Day)$.

处理市政污水以满足严格的污水质量标准是一个能源密集型过程，也是污水处理厂 (WWTP) 成本的主要来源。能源消耗（EC）的分析和预测对于设计和运营可持续节能污水处理厂至关重要。在本研究中，基于六年（2014-2019 年）收集的数据，研究了废水、水力和气候参数对东墨尔本污水处理厂 EC 日消耗量的影响。应用数据工程方法来组合来自不同资源的特征。为此，使用了四种不同的特征选择 (FS) 算法来揭示这些变量之间的关系，并选择最相关的变量来训练机器学习 (ML) 模型。更远，研究了人工神经网络 (ANN) 和梯度提升机 (GBM) 和随机森林 (RF) 的两种决策树算法的应用，以预测 EC 记录，然后进行 95% 置信区间评估。FS 算法的结果表明，总氮、化学需氧量 (COD) 和流入流量对污水处理厂能耗的影响最大。此外，GBM 在所有其他回归算法中具有最佳的性能预测。95% 的置信区间显示出合理的预测误差带（入流量对污水处理厂能耗的影响最大。此外，GBM 在所有其他回归算法中具有最佳的性能预测。95% 的置信区间显示出合理的预测误差带（入流量对污水处理厂能耗的影响最大。此外，GBM 在所有其他回归算法中具有最佳的性能预测。95% 的置信区间显示出合理的预测误差带（$\pm 68米宽H/D\mathrm{一种}是)$.