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

TL;DR

This paper introduces the Stochastic Coefficient of Variation (sCV) and Forecastability (F) metrics to better quantify solar irradiance variability and forecastability, addressing limitations of traditional measures and supporting improved energy management strategies.

Contribution

The paper develops a novel framework using sCV and F metrics that effectively isolate stochastic fluctuations and incorporate temporal dependencies in solar irradiance analysis.

Findings

sCV and F accurately capture multi-scale fluctuations

The metrics outperform traditional variability measures

Framework validated with real meteorological data

Abstract

This work presents a robust framework for quantifying solar irradiance variability and forecastability through the Stochastic Coefficient of Variation (sCV) and the Forecastability (F). 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, sCV provides a normalized, dimensionless measure of variability that theoretically ranges from 0 to 1. F extends sCV by integrating temporal dependencies via maximum autocorrelation, thus linking sCV with F. The proposed methodology is validated using synthetic cyclostationary time series and experimental data from 68 meteorological stations (in Spain). Our comparative analyses demonstrate that sCV and F 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 allocation