Advancing Solar Power Forecasting: Integrating Boosting Cascade Forest and Multi-Class-Grained Scanning for Enhanced Precision

Abstract

Accurate solar power generation forecasting is paramount for optimizing renewable energy systems and ensuring sustainability in our evolving energy landscape. This study introduces a pioneering approach that synergistically integrates Boosting Cascade Forest and multi-class-grained scanning techniques to enhance the precision of solar farm power output predictions significantly. While Boosting Cascade Forest excels in capturing intricate, nonlinear variable interactions through ensemble decision tree learning, multi-class-grained scanning reveals fine-grained patterns within time-series data. Evaluation with real-world solar farm data demonstrates exceptional performance, reflected in low error metrics (mean absolute error, 0.0016; root mean square error 0.0036) and an impressive R-squared score of 99.6% on testing data. This research represents the inaugural application of these advanced techniques to solar generation forecasting, highlighting their potential to revolutionize renewable energy integration, streamline maintenance, and reduce costs. Opportunities for further refinement of ensemble models and exploration of probabilistic forecasting methods are also discussed, underscoring the significance of this work in advancing solar forecasting techniques for a sustainable energy future.