A Novel Spectral Correction Method for Predicting the Annual Solar Photovoltaic Performance Ratio Using Short-Term Measurements

A novel spectral-corrected Performance Ratio calculation method that aligns the short-term Performance Ratio calculation to the annual calculated Performance Ratio is presented in this work. The spectral-corrected Performance Ratio allows short-term measurements to reasonably estimate the annual Performance Ratio, which decreases the need for long-term measures and data storage and assists with routine maintenance checkups. The piece-wise empirical model incorporates two spectral variables, a geographical location-based variable, the air mass, a PV-technology-based variable, and a newly defined spectral correction factor that results in a universal application. The spectral corrections show significant improvements, resulting in errors across different air mass and clearness index ranges, as well as temporal resolutions. The results indicate that a spectral correction methodology is possible and a viable solution to estimate the annual Performance Ratio. The results further indicate that by correcting the spectrum, short-term measurements can be used to predict the annual Performance Ratio with superior performance compared to the well-known normal and weather-corrected PR calculation methods. This approach is the first documented effort to address the spectrum’s influence on the utility-scale Performance Ratio calculation from hourly measurements. The empirical formula suggested for the Performance Ratio calculation can be of extreme value for the real-time monitoring of PV systems and enhancing PV power forecasting accuracy when the spectrum is considered instead of its usual omission. The model can be universally applicable, as it incorporates location and technology, marking a groundbreaking start to comprehending and incorporating the spectral influence in utility-scale PV systems. The novel calculation has widespread application in the PV industry, performance modelling, monitoring, and forecasting.