To enhance accuracy, the solar power prediction is conducted through the application of the innovative gradient boosting regressor algorithm, contrasting its performance with that of the K nearest neighbors classifier algorithm.

The major goal is to boost accuracy by comparing the Novel Gradient Boosting Regressor algorithm and the K Nearest Neighbours Classifier approach for analysis and prediction of solar power generation. Materials and Methods: To evaluate, compare, and understand the effectiveness of recommended algorithms, a total of sixty samples were gathered for the two study groups' SPSS analysis. Thirty samples from group 2's K Nearest Neighbours Classifier method and thirty samples from group 1's Novel Gradient Boosting Regressor (GBR) algorithm underwent an 80% power G-power analysis. The accuracy rates observed were 78.6% for the K Nearest Neighbours (KNN) Classifier and 93.3% for the Novel Gradient Boosting Regressor. Consequently, it can be concluded that the GBR method outperforms the KNN Classifier algorithm significantly. [ABSTRACT FROM AUTHOR]