Enhanced Site Adaptation Method for Reducing Uncertainties in Long-Term Photovoltaic Plant Performance Assessment

Abstract Accurate assessment of solar resources is critical for photovoltaic (PV) project feasibility and energy auctions, yet satellite-based estimates can differ significantly from ground measurements, creating uncertainty and motivating the need for reliable site adaptation methods. Within this context, the present article proposes a method for enhancing the linear regression site adaptation method by incorporating solar irradiance band separation, clear-sky classification index (Kc) analysis, and machine learning algorithms (artificial neural network (ANN)) while also evaluating the influence of irradiance data collection period and diverse climatic conditions validation. Results show that relative root mean square error (rRMSE) improvements were site dependent: all methods improved rRMSE at sites with over 75% clear-sky days, while only ANN was effective at 50% clear-sky sites. For Florianópolis (Brazil), single-year analyses showed that the irradiance band method achieved the lowest relative mean bias error (rMBE) in 50% of cases, compared to 33% for linear regression and 17% for ANN. The findings suggest that the proposed solar irradiance band classification is particularly well-suited for regions with stable and abundant solar resources. Additionally, results indicate that using two years of measured data would offer significant improvements over a one-year period and could reduce uncertainties for long-term PV plant performance assessment and aid implementation by industry planners and researchers in both government and nongovernment organizations.