How do I limit the predictions to 1 hour ahead instead of 48 hours?

[KelRem]

Im trying to limit the prediction time from being 48 hours ahead to only 1 hour ahead and also have it go in 30 minute intervals. Im trying to get the PV forecast for multiple different sites and if I try to do any more than 9 sites my computer runs out of ram due to the vast amount of predictions being generated.

[peterdudfield]

Hi @KelRem, thanks so much for asking the question

1. Different forecast horizons and intervals: The easiest is probably to use the forecast as it is, and then reduce the results to only 1 hour ahead and/or in 30 mins intervals. Other you would need to look into the code in here, but that might be quite tricky

2. N sites How much ram do you have? Are you running these forecast in series or in parrellel?

[KelRem]

I have 16 GB of ram, how do I tell weather I am running the code in series or in parallel. I assume its in parallel if I am running out of ram.

[peterdudfield]

Are you running them at the same time or running them one after each other?

can you past your code your are using?

[KelRem]

`from quartz_solar_forecast.forecast import run_forecast from quartz_solar_forecast.pydantic_models import PVSite from datetime import datetime, timedelta import pandas as pd import geocoder import serial

def generate_forecasts(sites_info, forecast_date): """Generate forecasts for multiple PV sites.

This function takes a list of site information tuples and a forecast date as input. For each site, it creates a PVSite object,
runs the forecast using the `run_forecast` function from the `quartz_solar_forecast` module, and generates a DataFrame
containing the site's latitude, longitude, capacity, and power forecast values. Finally, it concatenates all the site
DataFrames into a single DataFrame and returns it.

Args:
    sites_info (list): List of tuples containing site information. Each tuple should be in the format
        (pv_id, latitude, longitude, capacity). Latitude and longitude are geographic coordinates, and capacity
        is the site's capacity in kilowatts peak (kWp).
    forecast_date (str): Date for which the forecast is generated (format: "YYYY-MM-DD").

Returns:
    pandas.DataFrame: DataFrame containing forecasts for each PV site. The DataFrame has columns for each site's
        latitude, longitude, capacity, and power forecast, with a column name in the format "{pv_id} Power".
        The index of the DataFrame is set to the forecast dates.
"""
all_forecasts = []  # List to store DataFrames for each site

# Loop through each site information
for site_info in sites_info:
    # Unpack site information from the tuple
    pv_id, latitude, longitude, capacity = site_info

    # Create PVSite object for the site
    site = PVSite(latitude=latitude, longitude=longitude, capacity_kwp=capacity)

    # Run forecast for the site
    forecast = run_forecast(site=site, ts=forecast_date)

    # Flatten forecast values to a 1D array
    forecast_values = forecast.values.flatten()

    # Extract forecast dates from the forecast index
    forecast_dates = forecast.index

    print(forecast)
    print(forecast_dates)

    # Create a DataFrame for the current site
    site_df = pd.DataFrame(
        {
            f"{pv_id}_latitude": [latitude]
            * len(forecast_dates),  # Repeat latitude for each forecast date
            f"{pv_id}_longitude": [longitude]
            * len(forecast_dates),  # Repeat longitude for each forecast date
            f"{pv_id}_capacity": [capacity]
            * len(forecast_dates),  # Repeat capacity for each forecast date
            f"{pv_id} Power": forecast_values,  # Power forecast column with renamed header
        },
        index=forecast_dates,
    )

    # Append the site DataFrame to the list
    all_forecasts.append(site_df)

# Concatenate all site DataFrames into a single DataFrame along the column axis
master_df = pd.concat(all_forecasts, axis=1)

return master_df

if name == "main": sites_info = [] site_num = 1

g = geocoder.ip('me')#this function is used to find the current information using our IP Add

#if g.latlng is not None: #g.latlng tells if the coordiates are found or not
#    coordinates = g.latlng
#if coordinates is not None:
#    latitude, longitude = coordinates
#    print("Lat: " + str(latitude))
#    print("Long: " + str(longitude))
long = 25.02278
lat = 121.32058
x = -0.00003472222 * site_num #*4 = 0.00013888888
num = 1
while x <= 0.00003472222 * site_num:
    y= -0.00002525252 * site_num #*4 = 0.0001010101
    while y <= 0.00002525252 * site_num:
        sites_info.append(
        (
            "Site " + str(num),
            long + x,
            lat + y,
            0.0025,
        ),) # Site 1 information (pv_id, latitude, longitude, capacity)
        y += 0.00002525252
        num += 1
    x += 0.00003472222

# Example input for multiple PV sites
#sites_info = [
#    (
#        "Site1",
#        51.75,
#        -1.25,
#        0.0025,
#    ),  # Site 1 information (pv_id, latitude, longitude, capacity)
#    (
#        "Site2",
#        52.0,
#        -1.5,
#        0.0025,
#    ),  # Site 2 information (pv_id, latitude, longitude, capacity)
#    # Add more sites as needed, with format (pv_id, latitude, longitude, capacity)
#      (
#        "Site3",
#        19,
#        -10.5,
#        0.0025,
#    ),  # Site 3 information (pv_id, latitude, longitude, capacity)
    # Add more sites as needed, with format (pv_id, latitude, longitude, capacity)

#]
forecast_date = str(datetime.today())[:19].replace(")","").replace("(","").replace(",","").replace("'","")  # Forecast date
output_file = "multi_site_pv_forecasts.csv"  # Output file name

# Generate forecasts for the given sites and forecast date
forecasts = generate_forecasts(sites_info, forecast_date)
print("Generating " + str((site_num * 2 +1)*(site_num * 2 +1)) + " PV forecasts")
print(forecasts)

# Save forecasts to a CSV file
forecasts.to_csv(output_file)

`

[peterdudfield]

Ok, thanks, so you are loop through each site one by one, so I think its running in series, not in parrellel. Im slightly confused why so much ram is being used.

How much ram is used if you do 1 site? or 8 sites?

[KelRem]

Ok so I just tried generating 120 sites at once and the problem seems to have solve itself, because my ram stayed at around 11 GB.

[peterdudfield]

hmm interestesing, shall we close this for now? Or you want to look a bit more?

[peterdudfield]

@all-contributors please add @KelRem for question

[allcontributors[bot]]

@peterdudfield

I've put up a pull request to add @KelRem! :tada: