Investigating methods to improve photovoltaic thermal models at second-to-minute timescales

TL;DR

This study develops and tests new methods to enhance the accuracy of thermal models for photovoltaic modules at short timescales, incorporating wind effects and dynamic adjustments based on measured data.

Contribution

Introduces a conceptual RC model capturing wind effects and a dynamic modeling approach using EWM and FEM to improve PV thermal predictions.

Findings

FEM reduces RMSE and MAE errors significantly.

Average thermal time constant τ is about 6.3 minutes.

Proposed models outperform existing benchmarks.

Abstract

This paper presents a range of methods to improve the accuracy of equation-based thermal models of PV modules at second-to-minute timescales. We present an RC-equivalent conceptual model for PV modules, where wind effects are captured. We show how the thermal time constant $\tau$ of PV modules can be determined from measured data, and subsequently used to make static thermal models dynamic by applying the Exponential Weighted Mean (EWM) approach to irradiance and wind signals. On average, $\tau$ is $6.3 \pm 1~$min for fixed-mount PV systems. Based on this conceptual model, the Filter- EWM - Mean Bias Error correction (FEM) methodology is developed. We propose two thermal models, WM1 and WM2, and compare these against the models of Ross, Sandia, and Faiman on twenty-four datasets of fifteen sites, with time resolutions ranging from 1$~$s to 1$~$h, the majority of these at 1$~$min resolution. The FEM methodology is shown to reduce model errors (RMSE and MAE) on average for all sites and models versus the standard steady-state equivalent by -1.1$~$K and -0.75$~$K respectively.