Abstract

Energy consumption is vital to the global costs of wastewater treatment plants (WWTPs). With the increase of installed WWTPs worldwide, the modeling and forecast of their energy consumption have become a critical factor in WWTP design to meet environmental and economic requirements. Energy consumption in WWTPs is influenced by different biological and environmental factors, making it complicated and challenging to forecast. In this project, the student will perform real data analysis, and learn how to employ machine learning methods to forecast WWTP energy consumption. Different machine learning methods will be investigated and compared in this study.

Deliverables

Compare and investigate the forecasting performance of different machine learning methods. The output of this project can be a small research paper.