NOVELTY - The method involves receiving a power plant data set (122) and an environmental data set (148) as inputs to a processor, where the environmental data set has at least one of observed or expected environmental data. The power plant and environmental data sets are processed on the processor using one or more hybrid predictive models (150). At least one prediction (144) of the parameter of interest is generated, as an output of the processor, using the one or more hybrid predictive models. The power plant and the environmental data sets are cleansed prior to subsequent processing.    USE - Method for predicting parameter of interest for power plant such as fossil fuel power plant, wind power plant, nuclear power plant, and/or solar power plant.    ADVANTAGE - A hybrid approach creates a self-adjusting and self-monitoring system, which minimizes need for human interaction since the degraded models can be re-tuned to provide more accurate predictions. The use of a corrector model in connection with a static model reduces/eliminates the need to periodically re-baseline and re-tune the static model resulting in minimal downtime for plant equipment. A hybrid modeling approach can perform better. Use of the static model initially allows establishment of a prediction baseline without any prior training. The static model can be trained over the expected range of operating conditions creating a performance baseline over the entire operating envelope, thus minimizing required extrapolation and offsetting plant operating data sparseness issues.    DETAILED DESCRIPTION - An INDEPENDENT CLAIM is included for processor-implemented predictive model.    DESCRIPTION OF DRAWING(S) - The drawing shows a schematic diagram system for generating a prediction for power plant performance, availability, or degradation.    System for generating prediction for power plant performance, availability and degradation (120)    Power plant data set (122)    Prediction of parameter of interest (144)    Environmental data set (148)    Hybrid predictive model (150)