Abstract
Accurate forecasting of solar energy is essential for balancing supply and demand, enhancing energy planning, and supporting the integration of renewable resources into modern electricity grids. While recent research has heavily focused on machine learning-based models such as Long Short-Term Memory networks for solar energy forecasting, these approaches often lack transparency and interpretability. This study presents an interpretable by design photovoltaic (PV) forecasting framework that couples hierarchical factor analysis (HFA) with ridge regression. HFA compresses high dimensional meteorology into three physics meaningful second order factors after which a single parameter ridge model provides coefficient level transparency and regularization in this compact space. Using 15 min measurements from a 93.6 kWp plant in Adıyaman, Türkiye (May 17, 2021-Jan 12, 2025), we evaluate under a unified chronological split (0.64/0.16/0.20). The model combines strong generalization with clear insights into how meteorological variables affect solar power generation, ensuring transparency and verifiability. These results highlight regression-based methods as robust, explainable alternatives to complex deep learning models in photovoltaic forecasting.Since development and forecasting using highly multivariate models is typically not an easy task, our approach is designed to provide a more streamlined model through which future prediction is easier. Simplifying complexity and making it easier to understand how parameters affect the result, our proposed model simplifies finding the most important drivers of solar power generation.