ckoessler

Hy, I would like to know if it is possible to use PVsyst for modeling PV plant outcome based on spectral radiance datasets. Usually, we use broadband irradiation datasets. PVsyst is affecting a spectral correction model to correct this data in order to get the part of the radiation that can actually be used by modules. Therefore spectral mismatch is a source of uncertainty. In order to limit uncertainty of modeling, we would like to know if is is actually possible to use data registered with a calibrated PV Reference Device in PVsyst. Do you agree on the fact, that overall uncertainty should be lower? Tank you for your answer.

When trying to import some of my meteo data via PVsyst standard format, decimals are not always considered. That should not be a format issue car for some of my files everything is working fine. But 5 of 10 years were not imported well. Is there a mail, I can sent a sample to, so that you could test data yourself (can't join the .csv)? The example concernes data from 2011. I also had problems with 2008,20102013 and 2014. Do you know this problem? How can I handle it? Thank you for your help. Best regards

The indicated time format for the import is "legal time", but there might be a problem with the time format for some years of our weather station data. Is is known that there is no impact on cumulative daily global radiation by the choice of TSV or UT. But there is actually an impact on cumulative hourly global radiation due to the time-shift. Can this have an impact on diffuse part calculation in PVsyst? Are you using the daily or hourly clearness index for calculating the diffuse part of radiation?

Would it be possible for you to detail briefly the internal PVsyst time? N.B. Here below,I attached the image I was talking about in my first post.

I have a question about the diffuse part calculation using the Liu-Jordan correlation. By analyzing the figure attached to this post, I noticed that I do not understand totally the procedures of PVsyst's algorithms when it comes to the diffuse correlation. The figure shows the scatter plot between the diffuse fraction (kd=DHI/GHI) and the clearness index (kt=GHI/GHIo) of two samples. these two samples are: a) Hourly data of global horizontal radiance (generic) (mean weather station hourly data over 10 years) b) Monthly data of global horizontal radiance based on a) . --> Hourly data is generated synthetically in PVsyst As we only provide global horizontal radiance, PVsyst calculates automatically the diffuse part. The question is now how this is actually done (step by step)? a) hourly generic data is used together with the clearness index ( hourly or daily) to calculate the diffuse radiance thanks to the Liu-Jordan hourly correlation. --> blue points in my figure b) monthly data is transformed into hourly data by PVsyst. then the Liu-Jordan hourly correlation is applied to the synthetic data. --> red points in my figure If this is true, so why is there a difference in the dispersion of the red and blue scatter plot?? It seams to me, that data based on monthly values (red) is some how normalized every month to reach some kind of target value. But this would imply that the diffuse part is calculated for the monthly values before generating hourly values. Is my assumption correct? Thank you for your explication.

Hy When importing hourly data sets ( i.e. long term correlations based on weather data from Meteo France)) I observed that average time shift on clear days is quite similar to the difference between TSV/UTC or the Time Equation in general. The following figure is an example: https://drive.google.com/open?id=0B_jtkQGMQ2rqdXZ5RHRXTFRnTlE&authuser=0 Data from Meteo France has UTC format.The clear sky model applied by PVsyst is based on astrological algorithms. Consequently, there might be a difference between your model and observations made by Meteo France. Therefor I'd like to know if this observation can have an impact on simulation results? If I am not mistaken, differences between your clear sky model and hourly data for global irradiation provoke a difference in the diffuse fraction calculation. Is there a way to minimize errors or can we estimate the uncertainty of results due to this problem?

Which tool are you using for importing these data ? --> Import meteo data (external data source: PVsyst standard format) (PVsyst standard format for hourly meteo data) Which value are you replacing (GlobH, DiffH, temp) ? --> We got only hourly global values and hourly temperature values. How much values are to be replaced ? we use yearly data sets --> 8760 values. Depending on the data set, between 1 and 250 of values are missing. These values would need to be generated --> Keeping "-99" values brings down power outcome. replacing missing values with 0 is not the best way of handling things. What would the synthetique generation look like? Simple curve fitting? As PVsyst simulation requires complete data (for relevant monthly or yearly sums) I cannot suppress the concerned values??? Kind regards

Juste one additional information: - I actually replaced missing date with "-99" before importing it...when using the imported data for simulation ,the "-99" values hadn't been replaced, which brings down power outcome....

Hy, I want to use hourly measurements to simulate power outcome of our projects. Concerning missing data ,the help file for PVsyst standard file imports says the following: The PVsyst simulation requires complete data (for relevant monthly or yearly sums). Missing data should be -99, they will be replaced by modeled values (synthetic generation). Here my question: - Do I need to execute the synthetic generation? I am asking because when checking the imported data, there are still the "-99" values when data is missing. I tried to use imported data with ' hourly synthetic generation'. Bus this is using my monthly values to generate a new hourly data set rather than to just replace the "-99". What is the right way to generate just the missing data? Tank you a lot!