Research on Coal Calorific Value Prediction Based on K-Means Clustering and Ridge Regression Algorithm

Abstract

ObjectivesThe calorific value of coal is one of the important evaluation criteria for measuring coal quality, and is also the main basis for valuation of power coal. In order to realize high-precision and rapid prediction of coal calorific value while reducing prediction costs, a new prediction method is proposed.MethodsThe K-Means clustering algorithm is used to cluster similar coal types. The sample data comes from 4 269 entry testing information from a self-contained coal yard of a power plant in Shandong in the past 6 years. On the basis of clustering, Ridge regression models are established from industrial analysis data to received base calorific value, which is used as a prediction model for coal calorific value.ResultsThe established K-Means clustering and Ridge regression hybrid model exhibits excellent prediction performance. Compared with the traditional multiple linear regression model, this hybrid model can reduce the mean absolute error by up to 30.525%, reduce the root mean square error by up to 60.054%, and increase the correlation coefficient by up to 2.320%.ConclusionsThe mixed model of K-Means clustering and Ridge regression reduces the prediction cost of coal calorific value, and also improves the accuracy and speed of prediction, providing a new idea for predicting coal calorific value