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#Initialize the mode here

model = GFS(resolution='half', set_type='latest')

#the location I want to forecast the irradiance, and also the timezone

latitude, longitude, tz = 15.134677754177943, 120.63806622424912, 'Asia/Manila'

start = pd.Timestamp(datetime.date.today(), tz=tz)

end = start + pd.Timedelta(days=7)

#pulling the data from the GFS

raw_data = model.get_processed_data(latitude, longitude, start, end)

raw_data = pd.DataFrame(raw_data)

data = raw_data

#Description of the PV system we are using

system = PVSystem(surface_tilt=10, surface_azimuth=180, albedo=0.2,
                                 module_type = 'glass_polymer',
                                 module=module, module_parameters=module,
                                 temperature_model_parameters=temperature_model_parameters,
                                 modules_per_string=24, strings_per_inverter=32,
                                 inverter=inverter, inverter_parameters=inverter, 
                                 racking_model='insulated_back')

#Using the ModelChain

mc = ModelChain(system, model.location, orientation_strategy=None,
                aoi_model='no_loss', spectral_model='no_loss',
                temp_model='sapm', losses_model='no_loss')

mc.run_model(data);

mc.total_irrad.plot()

plt.ylabel('Plane of array irradiance ($W/m^2$)')

plt.legend(loc='best')

Here is the picture of it

I am actually getting the same values for irradiance for days now. So I believe there is something wrong. I think there should somewhat be of different values for everyday at the least

Forecasting Irradiance

Marc Valencia
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  • @kevinsa5 These are the following predicted vs actual (on the outer right). I at least thought the predicted values should be like the trend of the actual Predicted Actual 2021-07-19 09:00:00 3829.047806 2998.296 2021-07-19 12:00:00 5760.633125 4018.059 2021-07-19 15:00:00 4001.642488 3072.624 2021-07-20 09:00:00 3836.71381 613.4196 2021-07-20 12:00:00 5738.273921 2856.806 2021-07-20 15:00:00 3991.942266 2212.466 2021-07-21 09:00:00 3835.874692 0 2021-07-21 12:00:00 5744.592165 1549.578 2021-07-21 15:00:00 4004.058573 2216.023 – Marc Valencia Jul 21 '21 at 23:39

1 Answers1

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I think the reason the days all look the same is that the forecast data predicts those days to be consistently overcast, so there's not necessarily anything "wrong" with the values being very similar across days -- it's just several cloudy days in a row. Take a look at raw_data['total_clouds'] and see how little variation there is for this forecast (nearly always 100% cloud cover). Also note that if you print the actual values of mc.total_irrad, you'll see that there is some minor variation day-to-day that is too small to appear on the plot.

kevinsa5
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  • I am sorry for the previous comment, I am actually new here. It's actually rainy now here in the Philippines and I thought the output should be of the same trend at least. The prediction shows an output, while the actual value has no solar output. Do you have any idea on how can I at least get more accurate at this? – Marc Valencia Jul 21 '21 at 23:46
  • @MarcValencia what do you mean by "the actual value"? A real solar array will still produce a little bit of power even on rainy days. I suggest reading some of the scientific literature on PV forecasting to see how other people do it. – kevinsa5 Jul 22 '21 at 15:04
  • The predictions for PVLIB are very far from what is the actual power we got. But yeah, Maybe, I will try some other methods too. Thanks for the help! – Marc Valencia Jul 23 '21 at 05:27