Short-term forecasting for supercharged boiler safety performance based on advanced data-driven modelling framework

The safety of boilers is directly related to the operation of steam power plants. Consequently, accurate short-term forecasting model is needed for the robust control of important safety performance parameters. In this study, an advanced data-driven modelling framework based on neural networks is proposed to establish short-term forecasting (STF) models for three safety indicators of a supercharged boiler: main steam temperature (MST), drum pressure (DP) and drum level (DL). To overcome the challenges of external noise and non-stationarity, locally weighted regression (LWR) and differencing method are introduced to improve the model performance. Convolutional neural network (CNN) and long short-term memory network (LSTM) are proved as practical options for STF modelling, while CNN has a lower computational cost. The models show excellent performance on a real dataset containing multiple dynamic processes: the mean absolute percentage errors of the three targets are 0.0041%, 0.0355% and 0.0221% respectively and the forecasting horizon is future 60 timesteps. The sensitivity of STF models to outliers is analyzed. The results show that the models are not sensitive to eroded input. The proposed framework and methods can be a preferential solution to the forecasting modelling of power systems. (C) 2021 Elsevier Ltd. All rights reserved.