Recurrence Quantification Analysis Applied to Energy Production in Monocrystalline and Polycrystalline Solar Panels

Arellanos-Tafur E. · 2026 IEEE 15th International Conference on Communication Systems and Network Technologies, CSNT 2026 (2026) · DOI: 10.1109/csnt69054.2026.11502511

Resumen

This investigation implements Recurrence Quantification Analysis (RQA) to characterize the nonlinear temporal dynamics of photovoltaic energy generation systems using 313 daily observations recorded between January 5 and December 6, 2018. The RQA framework enables systematic quantification of recurrence patterns, deterministic structures, and dynamical complexity through metrics including recurrence rate (RR), determinism (DET), laminarity (LAM), and Shannon entropy (ENTR). Statistical analysis reveals that monocrystalline panels achieve a mean energy production of 606.95 Wh/m2, exceeding the polycrystalline output of 551.53 Wh/m2 by 10.05%, with standard deviations of 217.93 and 197.01 Wh/m2, respectively. RQA metrics yield RR values of 5.71% and 5.64% for monocrystalline and polycrystalline technologies, confirming comparable recurrence densities within the reconstructed phase space. Determinism registers 13.70% and 13.32%, indicating that monocrystalline systems exhibit a 2.86% enhancement in predictability. Laminarity attains 17.25% and 19.63%, respectively, revealing a 13.80% greater propensity for quasi-stationary states in polycrystalline configurations. A Pearson correlation coefficient of r = 0.9988 (p < 0.001) confirms near-perfect synchronization between both technologies, attributable to shared meteorological forcing. These findings establish RQA as an effective complementary framework for photovoltaic characterization, providing dynamical insights into predictability and operational stability that remain imperceptible to conventional statistical methods.

Instituciones: Universidad Continental