Research on Prediction Algorithm of Thermal Power Generation Steam Volume Based on Model Fusion

At present, the main power generation method in my country is thermal power generation, which is the core pillar of my country's energy. Combustion efficiency is a key factor in thermal power generation. Reducing energy consumption and improving the combustion efficiency of boilers are the main issues of current research. However, the combustion efficiency of the boiler is a process involving multiple variables, nonlinearity and high complexity, and it is difficult to find suitable process parameters based on experience and theory. With the development of artificial intelligence technology, intelligent learning algorithms can now be used to analyze and study the historical combustion data of boilers, so as to improve the problem of low combustion efficiency. In this paper, steam volume prediction and improvement of combustion efficiency as the starting point, with the historical operation data of the power plant as the research object, using the improved model fusion method for tuning and prediction, compared with multiple linear regression, support vector machine, tree model, through experiments to verify the effectiveness of the fusion algorithm.