An analytical model for predicting photovoltaic module key voltage parameters incorporating the temperature difference between the module and ambient

Abstract

Compared to controlled laboratory conditions, the electrical performance of photovoltaic (PV) modules under real operating conditions is influenced by the nonlinear effects of dynamic environmental factors. Improving voltage parameter accuracy, especially Maximum Power Point Voltage (V_mp), is crucial for efficient Maximum Power Point Tracking (MPPT) and overall system performance. Traditional models that rely solely on module temperature and irradiance fail to adequately capture outdoor climate variations. This paper proposes an analytical Model for predicting voltage parameters based on the temperature difference between the module and ambient(ΔT), considering their interaction. First, the strong nonlinear correlation between ΔT and voltage parameters was determined using correlation analysis methods such as the Maximal Information Coefficient (MIC). Then, ΔT was quantified and integrated into the traditional conversion formula. Finally, the formula coefficients were identified using the levenberg–marquardt (L-M) method with limited historical data. The proposed model was validated using data from nine PV module groups with six technologies under various climates from two public datasets. Results show that incorporating the ΔT formula improves prediction accuracy and environmental adaptability of traditional model. The RMSE for Open Circuit Voltage (V_oc) decreased by 0.1050 to 0.6389 V and for V_mp by 0.1004 to 1.2484 V, with the reduction in error being more especially significant under high-temperature conditions above 40 ℃. The identified coefficients show good stability and consistency. Furthermore, predictions for the same PV modules deployed under different climate conditions validated the model’s good generalization ability.