This study focuses on developing a universal methodology for day-ahead forecasting of solar photovoltaic power generation, addressing the variability of solar irradiance and its impact on electricity markets. The research aims to enable the selection of optimal forecasting methods based on data availability and quality. The proposed methodology encompasses data analysis, model selection, development, and validation, integrating physical and statistical approaches. Physical models transform solar irradiance into electrical output through sequential mathematical modeling, while statistical models leverage supervised machine learning, specifically Multilayer Perceptron and Gradient Boosting Regression. The methodology was validated using the PVOD dataset from China and operational data from Russian PV plants, achieving normalized root mean square errors of 5.41–6.07% for physical models and 9.5–10.2% for statistical models. Forecasting skill scores of 0.731–0.742 demonstrate superior performance over naive day-ahead forecasts. The approach ensures adaptability to diverse data scenarios, supporting PV plant operators and grid dispatch centers in optimizing bidding strategies within electricity markets, such as Russia’s Wholesale Electricity and Capacity Market. This methodology offers a scalable solution for enhancing the reliability of solar power integration into power systems.