Multi-Factor Carbon Emissions Prediction in Coal-Fired Power Plants: A Machine Learning Approach for Carbon Footprint Management

Abstract

In coal-fired power plants, accurately accounting for carbon footprints is crucial for reducing greenhouse gas emissions and achieving sustainability goals. Life cycle assessment (LCA) is a comprehensive approach that expands the scope of carbon accounting, enabling the calculation of carbon emission data. However, the unclear boundary definition and incomplete data types often lead to insufficient accuracy in model calculations and predictive performance. Herein, we developed machine learning models to predict carbon emissions in a 1000 MW coal-fired power plant. The ElasticNet modeling approach demonstrated exceptional predictive accuracy (R2 = 0.9514; MAE = 435.42 metric tons CO2). Coal combustion constituted the predominant source of greenhouse gas emissions, with quarterly emissions reaching 1.63 million metric tons in Q1 and 1.11 million metric tons in Q3. Emission intensity exhibited remarkable stability across operational load ranges (1.0–1.1 kg/MWh). Notably, under high-load conditions (>70%), low-calorific-value coal generated marginally higher specific emissions (1.11 kg/MWh) compared to high-calorific-value coal (1.05 kg/MWh). The findings provide rational strategies for optimizing coal procurement strategies and environmental control measures, thereby facilitating an optimal balance between operational efficiency and environmental stewardship.