  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.
