Unlocking the Potential of Wastewater Treatment: Machine Learning Based Energy Consumption Prediction

Abstract

Wastewater treatment plants (WWTPs) are energy-intensive facilities that fulfill stringent effluent quality norms. Energy consumption prediction in WWTPs is crucial for cost savings, process optimization, compliance with regulations, and reducing the carbon footprint. This paper evaluates and compares a set of 23 candidate machine-learning models to predict WWTP energy consumption using actual data from the Melbourne WWTP. To this end, Bayesian optimization has been applied to calibrate the investigated machine learning models. Random Forest and XGBoost (eXtreme Gradient Boosting) were applied to assess how the incorporated features influenced the energy consumption prediction. In addition, this study investigated the consideration of information from past data in improving prediction accuracy by incorporating time-lagged measurements. Results showed that the dynamic models using time-lagged data outperformed the static and reduced machine learning models. The study shows that including lagged measurements in the model improves prediction accuracy, and the results indicate that the dynamic K-nearest neighbors model dominates state-of-the-art methods by reaching promising energy consumption predictions.

Analysis and Prediction of Energy Consumption in Neural Networks Based on Machine Learning

“…Research has shown that machine learning methods have been widely applied to energy consumption modeling [1,2] , such as electricity [3] , wastewater treatment [4] , complex networks [5] , sensor parameters [6] , and more. Of course, there are also many applications in neural networks.…”

A Study on Developing an AI-Based Water Demand Prediction and Classification Model for Gurye Intake Station

“…Random forest (RF) is an ensemble-based model and a classification model that adds voluntariness and the basic principle of bootstrap aggregation (bagging), which is a method of aggregating samples by learning bootstrap models several times in multiple decision tree models. Random forest has high accuracy among classification models [22,[36][37][38][39]. Random forest randomly extracts learning data based on the basic principle of bagging, independently constructs a decision tree, and generates a total of n-trees.…”

“…The confusion matrix is true positive (TP) when an observed value is predicted as 1 and the model result is 1, false negative (FN) when the observed value is predicted as 1 and the model result is 0, and the observed value is 0. Predicting the model output value as 1 is called false positive (FP), and when the observed value is 0, predicting the model output value as 0 is called true negative (TN) [6,15,39].…”

Machine Learning-Based Energy Consumption Estimation of Wastewater Treatment Plants in Greece

“…Comprehending the energy consumption (EC) of a wastewater treatment plant (WWTP) is crucial for various reasons [1]. Efficient energy management plays a direct role in enhancing cost-effectiveness by facilitating optimal resource allocation and the potential for savings.…”

“…Beyond financial implications, a grasp of EC knowledge contributes to environmental sustainability and sustainable development by mitigating the carbon footprint linked with elevated energy use. Precise energy data facilitate regulatory compliance, while well-informed infrastructure planning, benchmarking, and funding decisions draw advantages from this comprehension [1]. Transparent endeavors to manage EC not only enhance public perception but also foster community engagement, aligning WWTPs with responsible resource stewardship and sustainable practices.…”