Abstract
It is important to predict air pollutant emissions from coal-fired power plants using real-time technological parameters to improve environmental efficiency. Since the relationship between emissions and parameters is nonlinear, machine learning models are needed to forecast emissions under various boiler operating modes. This study develops and tests Deep Sparse Transformer Networks for predicting pollutant time series, accounting for long-term dependencies. Data were collected from a 4000 MW coal-fired power plant in Ekibastuz, Kazakhstan, covering 67,527 records for 14 indicators at 10 min intervals. Fractal R/S analysis confirmed long-term memory in SO2, PM2.5, and NOx series, guiding window length selection. The results show that the model achieves slightly better accuracy for SO2 (R2 0.95–0.38), while NOx and PM2.5 have similar dynamics (R2 0.93–0.26). However, accuracy drops notably after 12 points, making the model best suited for short-term forecasts. These findings support environmental monitoring services and help optimize plant parameters, contributing to lower emissions and advancing carbon neutrality goals.