Prediction of CO–NOxEmissions from a Natural Gas Power Plant Using Proper Machine Learning Models

Abstract

Four machine learning (ML) models including a deep neural network, a long short‐term memory network, a random forest (RF), and an extreme gradient boosting are implemented to predict CO–NOxemissions from a natural gas power plant. A new feature optimization scheme (FOS) via a sequencing process of feature selection and hyperparameter optimization can intensify the ML models. Through the procedures of training, validation, and testing, reliable ML models need to take high prediction accuracy and fast training into account. After a few comparisons, it is found that 1) the FOS effectively improves the prediction accuracy by 18%–67%; 2) the FOS‐based RF model is an appropriate option to carry out the fast and accurate prediction of CO–NOxemissions by using the decision tree classifiers.