SC-XGBoost based soft measurement method for coal low heat value in power station

Abstract

With the country vigorously promoting structural reform on the supply side of energy, the installed capacity of new energy sources has been rising and competition in the power market has become increasingly fierce. On the other hand, the complexity and volatility of the global coal market has led to a rise in the cost of power generation enterprises using coal as their energy source. Coal heat value is one of the most important evaluation criteria for coal quality and is also the most important basis for coal procurement. Accurate prediction of coal heat value can effectively control power plant operation and procurement costs. In order to achieve efficient prediction of the heat value of coal, the Pearson coefficients were used to select the characteristics of the variables of interest, the DBSCAN algorithm was used to de-noise 1733 assay data of a power plant's own coal plant in the past two years, and spectral clustering (SC) analysis was performed on the de-noised data. The classified subsample sets were then used to build prediction models using the extreme gradient boosting (XGBoost) algorithm and compared with Ordinary Least Squares (OLS) and support vector machines (SVM) models. The performance of the models was compared with that of OLS and SVM. The results show that the accuracy of the XGBoost-based coal-fired heat value prediction model for power stations is significantly better than that of the other algorithms, and the generalization ability is stronger. The prediction model can further improve the refinement level of the model and provide a reliable and efficient method for coal-fired power station heat value prediction.

Keywords

• low heat value
• machine learning
• spectral clustering
• extreme gradient boosting(xgboost)
• soft measurements