Abstract
Based on the idea of knowledge distillation, a teacher-student ensemble method is proposed for UCFD (Unburned Carbon in Flue Dust) prediction when influencing factors have different sampling frequency. First, a multi-layer feed forward network is built as the teacher model. Loss function is customized to account for the sampling period discrepancy between outputs and inputs. With this teacher model, UCFD could have the same rate with its influencing factors. Second, Xgboost and Adaboost are used to form an ensemble student model to improve the training process and prediction robustness. Third, a power plant in Shandong province is chosen to make data experiment. Results illustrate that the teacher-student ensemble method can give a more accurate forecast.
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