Multivariate Gaussian Process Incorporated Predictive Model for Stream Turbine Power Plant

Steam power turbine-based power plant approximately contributes 90% of the total electricity produced in the United States. Mainly steam turbine consists of multiple types of turbine, boiler, attemperator, reheater, etc. Power is produced through the steam with high pressure and temperature that is conducted by the turbines. The total power generation of the power plant is highly nonlinear considering all these elements in the model. We perform a predictive modeling approach to detect the power generation from these turbines by the Gaussian process (GP) model. As there are multiple interconnected turbines, we consider a multivariate Gaussian process (MGP) modeling to predict the power generation from these turbines which can capture the cross-correlations between the turbines. Also, the sensitivity analysis of the input parameters is constructed for each turbine to find out the most important parameters.