A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler

• Published in: Energy (Oxford) 2013-06, Vol.55, p.319-329
• Main Authors: Lv, You, Liu, Jizhen, Yang, Tingting, Zeng, Deliang
• Format: Article
• Language: English
• Subjects: Applied sciences; coal; Coal-fired boiler; Energy; Ensemble learning; Exact sciences and technology; least squares; Least squares support vector machine; NOx emission; Partial least squares; power plants; prediction; Soft fuzzy c-means; support vector machines; systems engineering
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2013.02.062

Real operation data of power plants are inclined to be concentrated in some local areas because of the operators’ habits and control system design. In this paper, a novel least squares support vector machine (LSSVM)-based ensemble learning paradigm is proposed to predict NOx emission of a coal-fired boiler using real operation data. In view of the plant data characteristics, a soft fuzzy c-means cluster algorithm is proposed to decompose the original data and guarantee the diversity of individual learners. Subsequently the base LSSVM is trained in each individual subset to solve the subtask. Finally, partial least squares (PLS) is applied as the combination strategy to eliminate the collinear and redundant information of the base learners. Considering that the fuzzy membership also has an effect on the ensemble output, the membership degree is added as one of the variables of the combiner. The single LSSVM and other ensemble models using different decomposition and combination strategies are also established to make a comparison. The result shows that the new soft FCM-LSSVM-PLS ensemble method can predict NOx emission accurately. Besides, because of the divide and conquer frame, the total time consumed in the searching the parameters and training also decreases evidently. •A novel LSSVM ensemble model to predict NOx emissions is presented.•LSSVM is used as the base learner and PLS is employed as the combiner.•The model is applied to process data from a 660MW coal-fired boiler.•The generalization ability of the model is enhanced.•The time consuming in training and searching the parameters decreases sharply.