Stochastic Coefficient of Variation: Assessing the Variability and Forecastability of Solar Irradiance

Abstract

International audienceThis work presents a robust framework for quantifying solar irradiance variability and forecastability through the Stochastic Coefficient of Variation () and the Forecastability (). Traditional metrics, such as the standard deviation, fail to isolate stochastic fluctuations from deterministic trends in solar irradiance. By considering clear-sky irradiance as a dynamic upper bound of measurement, provides a normalized, dimensionless measure of variability that theoretically ranges from 0 to 1. extends by integrating temporal dependencies via maximum autocorrelation, thus linking with . The proposed methodology is validated using synthetic cyclostationary time series and experimental data from 68 meteorological stations (in Spain). Our comparative analyses demonstrate that and proficiently encapsulate multi-scale fluctuations, while addressing significant limitations inherent in traditional metrics. This comprehensive framework enables a refined quantification of solar forecast uncertainty, supporting improved decision-making in flexibility procurement and operational strategies. By assessing variability and forecastability across multiple time scales, it enhances real-time monitoring capabilities and informs adaptive energy management approaches, such as dynamic outage management and risk-adjusted capacity allocatio