Carbon Emission Prediction for Gas Power Plants Based on Deep Learning Under Small‐Sample Conditions

Accurate forecasting of carbon emissions from power generation enterprises is essential under China's dual‐control policy. Although deep learning methods show strong potential, studies on their optimal configuration remain limited. This paper proposed a hybrid deep learning framework integrating a convolutional neural network (CNN), bidirectional long short‐term memory (BiLSTM), and an attention mechanism for carbon emission prediction in natural gas power plants. The present study utilized two distinct optimization methodologies: a structured design strategy encompassing light, medium, and heavy configurations, while the other employed Bayesian optimization for hyperparameter tuning. The models were evaluated using 5‐fold cross‐validation on 619 operational samples from two 487.1‐MW condensing units in a power plant in Hainan, China. The medium configuration achieved the best balance between accuracy, efficiency, and stability, with R 2 = 0.9833, RMSE = 0.0342, and MAE = 0.0242. Under small‐sample conditions, the structured design approach outperformed Bayesian optimization by 0.16% in accuracy while requiring only 7.42% of the training time. The proposed framework provides an efficient and interpretable reference for selecting deep learning architectures in small‐sample industrial regression tasks and supports intelligent, low‐carbon power generation applications.