Energy consumption prediction in water treatment plants using deep learning with data augmentation

Abstract

Wastewater treatment plants (WWTPs) are energy-intensive facilities that play a critical role in meeting stringent effluent quality regulations. Accurate prediction of energy consumption in WWTPs is essential for cost savings, process optimization, regulatory compliance, and reducing carbon footprint. This paper introduces an efficient approach for predicting energy consumption in WWTPs, leveraging deep learning models, data augmentation, and feature selection. Specifically, Spline Cubic interpolation enriches the dataset, while the Random Forest model identifies important features. The study investigates the impact of lagged data to capture temporal dependencies. Comparative analysis of five deep learning models on original and augmented datasets from Melbourne WWTP demonstrates substantial performance improvement with augmented data. Incorporating lagged energy consumption data further enhances accuracy, providing valuable insights for effective energy management. Notably, the Long Short-Term Memory (LSTM) and Bidirectional Gated Recurrent Unit (BiGRU) models achieve Mean Absolute Percentage Error (MAPE) values of 1.36% and 1.436%, outperforming state-of-the-art methods.