From 1735bcee3179e4d192c4d970ced1a7290404ea60 Mon Sep 17 00:00:00 2001
From: JadeVexo <66781710+JadeVexo@users.noreply.github.com>
Date: Mon, 3 Jun 2024 13:23:08 -0400
Subject: [PATCH] feat: add generate_forecasts script (#99)

* feat: add generate_forecasts script

This commit introduces a new script named generate_forecasts.py that generates solar power forecasts for multiple photovoltaic (PV) sites.

The script contains a function called generate_forecasts that 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.

The script then concatenates all the site DataFrames into a single DataFrame and returns it. The resulting 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.

The script also includes an example usage section that demonstrates how to call the generate_forecasts function with sample site information and a forecast date. The generated forecasts are then saved to a CSV file named pv_forecasts.csv.

This script can be used to generate solar power forecasts for multiple PV sites, which can be useful for various applications, such as energy management, grid integration, and financial analysis.
---
 scripts/multi_site_generate_forecasts.py | 91 ++++++++++++++++++++++++
 1 file changed, 91 insertions(+)
 create mode 100644 scripts/multi_site_generate_forecasts.py

diff --git a/scripts/multi_site_generate_forecasts.py b/scripts/multi_site_generate_forecasts.py
new file mode 100644
index 00000000..d1b475f9
--- /dev/null
+++ b/scripts/multi_site_generate_forecasts.py
@@ -0,0 +1,91 @@
+from quartz_solar_forecast.forecast import run_forecast
+from quartz_solar_forecast.pydantic_models import PVSite
+import pandas as pd
+
+
+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
+
+        # 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__":
+    # Example input for multiple PV sites
+    sites_info = [
+        (
+            "Site1",
+            51.75,
+            -1.25,
+            1.25,
+        ),  # Site 1 information (pv_id, latitude, longitude, capacity)
+        (
+            "Site2",
+            52.0,
+            -1.5,
+            1.5,
+        ),  # Site 2 information (pv_id, latitude, longitude, capacity)
+        # Add more sites as needed, with format (pv_id, latitude, longitude, capacity)
+    ]
+    forecast_date = "2023-11-01"  # 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)
+
+    # Save forecasts to a CSV file
+    forecasts.to_csv(output_file)