Bagging-ELM Model for Heating Furnace Thermal Efficiency Prediction
Kai Shang, Xianglong Zeng, Xiaorui Dong · 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) · 2020
Thermal efficiency is a very important index for heating furnace in chemical industry. In order to predict the complicated variables, the Bagging-ELM network based on ensemble learning and Extreme Learning Machine is adopted. The proposed model is applied to predicting the net hourly electrical energy output in Combined Cycle Power Plant data set and the thermal efficiency of the heating furnace. There is a comparison between the results generated by Bagging-ELM model and those generated by BP model, RBF model, ELM model. Compared with other models, the Bagging-ELM can obtain higher accuracy and stability.