NOx emission prediction of coal-fired power units under uncertain classification of operating conditions

Abstract

Coal-fired power units are widely involved in peak shaving in the future renewable energy grid, leading to frequent changes in operating conditions, which makes it more difficult to predict NOx emissions. Aiming to address this challenge, a NOx emission prediction method based on a deep learning algorithm and uncertainty classification is proposed. Firstly, in order to better capture the intrinsic information in the NOx time series data, the deep learning network is used to construct the original NOx sequence prediction model. Secondly, a prediction error classification model based on a support vector machine is proposed to consider the uncertain working conditions and prediction errors of thermal power units. Then, the original sequence prediction results are corrected at different levels to obtain the final prediction results. The simulation experiments were compared with the operation data of a 660 MW subcritical boiler in the thermal power unit. Compared with several traditional models, the mean absolute percentage error, root mean square error, mean absolute error, and determination coefficient of the proposed model are 2.60 %, 11.09 mg/m3, and 8.27 mg/m3 and 0.86, respectively, which has higher prediction accuracy.