Stacked Irradiation Forecasting for Accurate POA Solar Prediction

The patent introduces a stacked generalization model as an intermediary between the meteorological observatory forecast data and the final POA irradiation forecast. This model includes multiple layers of generalizers that process and refine the raw forecast data, transforming it into more accurate intermediate and final results without requiring direct modification of the meteorological observatory's forecasting systems.

The forecast processing is divided into multiple segments or layers: the first-layer generalizer processes the raw forecast data to produce intermediate results, and the second-layer generalizer further refines these to produce the final POA irradiation forecast. This segmentation allows each layer to specialize in specific aspects of the forecasting task, improving overall accuracy while maintaining manageable complexity.

The first-layer generalizer performs preliminary processing of the raw forecast data from multiple information sources, producing intermediate forecast results that are already refined. This preliminary action reduces the complexity of the subsequent second-layer processing, allowing the system to handle multiple information sources efficiently without excessive processing overhead.

The stacked generalization model applies multiple layers of processing (excessive action) to the forecast data, where the first-layer generalizer applies one level of refinement and the second-layer generalizer applies additional refinement. This partial application of processing at different levels achieves high accuracy while avoiding the need to process all possible data transformations exhaustively.