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Instede of reading TMY file in to PvLib, I wants to generate weather data using PvLib function, class or modules.

I have found some of function to generate weather forecast using "from pvlib.forecast import GFS, NAM, NDFD, HRRR, RAP" these modules. Above mention method/algorithm has some limitation. It generate data for limited period. Some of the modules are generating only for 7 days or 1 months. Also it gives data for 3 hourly time stamp difference.

Is there any possibility to interpolate weather data for entire year using PvLib?

Shriganesh Patil
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  • Do you want to derive a synthetic timeseries from a TMY? Or do you want to have mean irradiance for each hour of a year? – dl.meteo Jun 29 '20 at 11:02
  • I want to use PvLib function/class etc to get weather data for entire year. – Shriganesh Patil Jul 01 '20 at 07:29
  • How to get TMY weather data (Historical Weather data) using PvLib weather models? – Shriganesh Patil Jul 01 '20 at 09:50
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    Pvlib provides a few functions to load data from pvgis or tmy2/tmy3 data from NREL. https://pvlib-python.readthedocs.io/en/stable/api.html#io-tools . What you really need is a provider of weather data like meteocontrol, solargis, solcast and others. – dl.meteo Jul 02 '20 at 19:38
  • but do we have any in build function generate historical weather data instead of loading using tmy2/tmy3? – Shriganesh Patil Jul 06 '20 at 15:23
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    PVlib is just a calculation tool where you have to put in your own data. – dl.meteo Jul 07 '20 at 14:32

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Forecast is generally meant to be used for future prediction, and is limited in time and accuracy inversely proportionate: longer future forecasts have less accuracy, and accuracy decreases the further in the future it is. For example, the forecast for today is more accurate than forecast for tomorrow, and so on. This is the reason that forecast is limited as you are forecasting for seven future days.

Forecast providers as GFS may or may not provide data for historic forecasts; it depends on the provider and their services.

As I remember, GFS gives prediction in old file fashion, so I moved to providers that gives online REST services forecast, as I become first a programmer and then a data scientist and never a meteorologist.

When timeseries period is not in your required period, you can do some resampling. Extra values will be mathematically calculated with some formula that—as long you don't know the original provider's formula—resample formula will be likely different.

Jeremy Caney
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kornel jadeit
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