Meta-machine learning framework for robust short-term solar power prediction across different climatic zones

The global energy landscape is increasingly dominated by solar power installations, driven by the sun's position as Earth's most abundant and sustainable energy resource. However, the intermittent nature of solar radiation, influenced by both astronomical cycles and meteorological conditions, creates significant challenges for reliable power generation and grid integration. To address the issue of uncertainty, this study proposes a robust and improved capacity machine learning framework with enhanced hypothesis functional space. The proposed model improves the capacity of an individual model by combining the hypothesis functions of individual machine learning models, increasing the representational capacity and hence the model's generalization. Moreover, a non-linear second stage is stacked to increase the depth of the proposed model, which utilizes meta-data of first stage to further improve the forecasting accuracy. Furthermore, the proposed model is validated on four different climatic zones of the world for solar power forecasting. The proposed model achieves an average improvement of 66.7% in mean absolute error across all locations compared to the next best performing algorithm, with particularly strong performance in arid zones. Statistical validation through Cook's distance analysis also confirms the model's reliability with an average of 8.64% influential points across all locations.