A Systematic Framework for Reconciling Solar Energy Production Models with Operational Data: A Literature Synthesis and Methodology Development

The growing deployment of utility-scale photovoltaic (PV) systems has heightened the importance of accurate energy yield predictions for financial planning and operational management. However, a persistent gap exists between predicted and actual performance, creating significant challenges for project developers, investors, and operators. This paper presents a comprehensive analysis of the performance gap phenomenon in solar PV systems through a systematic review of current literature and industry practices. We examine the primary sources of discrepancy between energy production models and operational data, including resource assessment uncertainties, system loss characterization, and operational factors. Building on this analysis, we develop a structured framework for reconciling predictions with real-world performance that integrates statistical approaches for uncertainty quantification with methodologies for model calibration. The proposed framework enables more accurate performance assessments through the systematic identification and correction of modeling biases across different temporal scales and operating conditions. This research contributes to improved risk assessment in PV project development and advances modeling methodologies by providing practitioners with a practical approach to align predictive models with operational realities, ultimately enhancing the reliability of financial models and performance guarantees in the solar energy sector.