Analysis and Forecasting of the Energy Consumption in Wastewater Treatment Plant

Abstract

Wastewater treatment plant (WWTP) is the energy-intensive industries. Energy is consumed at every stage of wastewater treatment. It is the main contributor to the costs of WWTP. Analysis and forecasting of energy consumption are critical to energy-saving. Many factors influence energy consumption. The relationship between energy consumption and wastewater is complex and challenging to identify. This article employed the fuzzy clustering method to categorize the sample data of WWTP and analyzed the relationship between energy consumption and the influence factors in different categories. The study found that energy efficiency in various categories was changed and the same influence factors in different types had different influence intensity. The Radial Basis Function (RBF) neural network was used to forecast energy consumption. The data from the complete set and categories was adopted to train and test the model. The results show that the RBF model using the date from the subset has better performance than the multivariable linear regression (MLR) model. The results of this study provided an essential theoretical basis for energy-saving in WWTP.