Decomposition and transposition models

[dina.christensen.martinsen]

I am currently working on an ongoing research project that involves analyzing decomposition and transposition models for Nordic climates.

Our results show that the default decomposition and transposition models in PVsyst yield higher uncertainties compared to alternative models for several of the analyzed locations. Specifically, the current options in PVsyst (Erbs and Perez) yield significantly higher uncertainties (rRMSD of 12,9% and rMBD of 4,3%) compared to other options with almost no bias in terms of rMBD and rRMSD below 10%.
Would it be possible to provide an option to perform the decomposition and transposition step separately and import a custom file containing not only GHI, but also DNI, DHI, and POA to PVsyst?

Alternatively, could you add more decomposition models and transposition models to choose from in PVsyst?

[Michele Oliosi]

Hello,

This sounds like a great research. Are you going to publish it? If so, we'd gladly go through it once it's out there.

In PVsyst you can always (and it is recommended to do so) import DHI or DNI in addition to GHI. This means that we do not need to rely on DirInt or Erbs for diffuse estimation.

Currently, it is also possible to import POA data as part of the custom file import (https://www.pvsyst.com/help/meteo_convdialog.htm). What this does is that it uses Hay for reverse transposition to get both GHI and DHI, and then uses these values for the simulation (as well as the Hay transposition). This means that the POA used in the simulation will be the imported one, however the decomposition in different irradiance components will be according to the Hay model.

We could add more decomposition / transposition models if necessary. We will establish this type of developments by first having a thorough look at the literature. I think your research would help us as a first step to assess the need to implement other transposition / decomposition models.

[James Barry]

On 1/10/2024 at 2:02 PM, dina.christensen.martinsen said:

ng research project that involves analyzing decomposition and transposition models for Nordic climates.

Our results show that the default dec

There is already a huge amount of literature on this topic. As a start I would recommend [1].

As is usually the case with models, the parameters have been tuned for certain locations, so it makes sense to test them at many locations. In general one can say that Erbs usually overestimates the diffuse component under clear sky conditions, and this bias is severe (of the order of 25% using 10 minute data from a location in Western Europe). You won't see that statistic when looking at all the data (all sky), but if you specifically look at clear sky conditions, you will see that basically Erbs overestimates the atmospheric turbidity / aerosol optical depth, leading to a higher diffuse component. Put another way, the sky is clearer than what Erbs models it to be, there is less scattering  due to aerosols and more direct light. This error leads to underestimation of the direct component and underestimation of shading losses in PVsyst

It would definitely make sense to include DIRINT [2] in PVsyst, since that model uses more predictors, not just the clearness index. Specifically, DIRINT has a time component that takes into account variability, i.e. the change in clearness index. That helps then to distinguish between different weather conditions, and thus model them differently.

All of that being said, the best is to import diffuse irradiance, which is however not always possible if it is not measured on site.

[1] Gueymard, C. A., & Ruiz-Arias, J. A. (2016). Extensive worldwide validation and climate sensitivity analysis of direct irradiance predictions from 1-min global irradiance. Solar Energy, 128, 1–30. https://doi.org/https://doi.org/10.1016/j.solener.2015.10.010

[2] Perez, R. R., Ineichen, P., Maxwell, E. L., Seals, R. D., & Zelenka, A. (1992). Dynamic global-to-direct irradiance conversion model. ASHRAE Transactions, 354–369.