Coal consumption prediction based on least squares support vector machine

Abstract

At present, China’s economic construction continues to move forward, and the demand for energy is increasing day by day, and the problem of energy shortage is showing. All industries are actively conducting research on energy saving and emission reduction. However, thermal power plants have large demand for coal and heavy pollution from flue gas. Therefore, technological upgrading, energy saving and operation control of coal-fired power plants need to be optimized. Support vector machine (SVM) algorithm is applied in the aspect of function fitting based on the strict statistics basis, and the coal consumption prediction model based on the least squares support vector machine is the product of the informatization application of power plant and the competition requirement of power market. Firstly, the research status of the optimization algorithm of thermal power unit is described. Secondly, the principle of support vector machine and least squares support vector machine is introduced, so as to do the basic work for establishing the prediction model of coal consumption later. Finally, the key problems to be further studied and solved in the field of coal consumption prediction in thermal power plants are discussed.