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

@@ -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)