A Hybrid Framework of Gradient-Boosted Dendritic Units and Fully Connected Networks for Short-Term Photovoltaic Power Forecasting

To ensure reliable and accurate short-term photovoltaic power generation prediction, this study introduces an integrated forecasting framework that combines the gradient boosting paradigm with a dendritic neural structure, termed Gradient Boosting Multi-Bias Dendritic Units Integrated in a Fully Connected Neural Network (GBMDF). The proposed GBMDF algorithm minimizes prediction deviations by progressively capturing the nonlinear mappings between residual predictions and environmental variables through an iterative error-correction process. Compared with traditional data-driven learning algorithms, GBMDF can comprehensively utilize multiple meteorological inputs while maintaining strong interpretability and analytical transparency. Furthermore, leveraging the flexibility of the GBMDF, the prediction accuracy of existing models is improved through a proposed compensation enhancement technique. Under this mechanism, GBMDF is trained to offset the residual differences in alternative predictors by examining the correlations between the error patterns of alternative predictors and weather attributes. This enhancement method features a simple concept and effective practical performance. Validation experiments confirm that GBMDF not only achieves higher accuracy in photovoltaic output prediction but also improves the overall efficiency of other forecasting methods.