Add refactored data processing module to reporting

Signed-off-by: Flora <[email protected]>

Author: flora-hofmann-frequenz

RELEASE_NOTES.md

@@ -11,6 +11,7 @@
 ## New Features
 
 - Added consistent logger setup across all modules for structured logging and improved observability. Example notebooks updated to demonstrate logger usage.
+- Added a `data_processing` module to `reporting` that provides a set of functions for processing, enriching, and analyzing time-series energy data from microgrid systems.
 
 ## Bug Fixes
 

src/frequenz/lib/notebooks/reporting/data_processing.py

@@ -0,0 +1,362 @@
+# License: MIT
+# Copyright © 2025 Frequenz Energy-as-a-Service GmbH
+
+"""Data processing utilities for microgrid energy reporting.
+
+This module provides a set of functions for processing, enriching, and analyzing
+time-series energy data from microgrid systems. It focuses on preparing data for
+PV (photovoltaic), battery, and grid energy flows, transforming it into a consistent
+structure for visualization, reporting, and analysis.
+
+Features
+--------
+- Enriches raw energy data with derived columns such as:
+  - PV production, self-consumption, feed-in, and battery charging.
+  - Net grid import and PV self-consumption share.
+- Handles time zone localization and conversion to Europe/Berlin.
+- Dynamically renames columns to more descriptive names, including
+  mapping component IDs (e.g., "PV #1", "Batterie #2").
+- Provides summary energy mix breakdowns (PV vs grid) in kWh, % share, and average kW.
+- Prepares tailored DataFrames for PV and battery analysis, supporting flexible
+  filtering by component.
+
+Main Functions
+--------------
+- `transform_energy_dataframe(df, component_types, mcfg)`:
+    Transforms a raw DataFrame with energy metrics into an enriched,
+    user-friendly format, adding PV, battery, and grid metrics.
+
+- `compute_power_df(main_df, resolution)`:
+    Computes total energy drawn from PV and grid sources over the given resolution,
+    returning a summary DataFrame with kWh, percentage, and average kW.
+
+- `print_pv_sums(main_df, resolution)`:
+    Prints total PV feed-in sums for each individual PV component
+    in a localized numeric format.
+
+- `create_pv_analysis_df(main_df, pv_filter, pvgrid_filter, pv_grid_filter_options)`:
+    Generates a DataFrame for PV analysis based on selected PV components
+    and whether to analyze PV alone, grid alone, or a grid/PV split.
+
+- `create_battery_analysis_df(main_df, bat_filter)`:
+    Creates a DataFrame for analyzing battery throughput, reshaping
+    it to long format for multi-battery analysis.
+
+Usage
+-----
+Typical usage involves:
+1. Loading a raw DataFrame with time-indexed energy measurements.
+2. Calling `transform_energy_dataframe` to process and enrich it.
+3. Using the resulting DataFrames to generate summaries,
+   for example with `compute_power_df`, `create_pv_analysis_df`, or
+   `create_battery_analysis_df` for visualization.
+"""
+
+from typing import Any, Dict, Iterable, List, Tuple, Union
+
+import pandas as pd
+
+# Constants
+TZ_NAME = "Europe/Berlin"
+COLUMN_TIMESTAMP = "timestamp"
+COLUMN_TIMESTAMP_NAMED = "Zeitpunkt"
+COLUMN_GRID = "grid"
+COLUMN_GRID_NAMED = "Netzanschluss"
+COLUMN_NET_IMPORT = "Netzbezug"
+COLUMN_CONSUMPTION = "consumption"
+COLUMN_CONSUMPTION_NAMED = "Brutto Gesamtverbrauch"
+COLUMN_BATTERY = "battery"
+COLUMN_BATTERY_POS = "battery_pos"
+COLUMN_BATTERY_NAMED = "Batterie Durchsatz"
+COLUMN_PV = "pv"
+COLUMN_PV_PROD = "PV Produktion"
+COLUMN_PV_NEG = "pv_neg"
+COLUMN_PV_EXCESS = "pv_excess"
+COLUMN_PV_FEEDIN = "PV Einspeisung"
+COLUMN_PV_SELF = "PV Eigenverbrauch"
+COLUMN_PV_BAT = "pv_bat"
+COLUMN_PV_IN_BAT = "PV in Batterie"
+COLUMN_PV_SHARE = "PV Eigenverbrauchsanteil"
+COLUMN_PV_THROUGHPUT = "PV Durchsatz"
+
+
+def transform_energy_dataframe(
+    df: pd.DataFrame,
+    component_types: List[str],
+    mcfg: Any,
+) -> Tuple[pd.DataFrame, pd.DataFrame]:
+    """Transform and enrich energy dataframe.
+
+    This function processes a raw DataFrame containing energy metrics,
+    adding derived columns for PV production, battery throughput, and grid metrics.
+
+    Args:
+        df: Raw DataFrame with energy metrics, expected to have a datetime index.
+        component_types: List of component types present in the DataFrame (e.g., ["pv", "battery"]).
+        mcfg: Microgrid configuration object providing component type IDs and other metadata.
+
+    Returns:
+        A tuple containing:
+        - `main_df`: A DataFrame with main columns for visualization and reporting.
+        - `df_renamed`: A fully enriched DataFrame with all derived columns and renamed
+          component IDs.
+    """
+    # Ensure the DataFrame has a datetime index
+    df = df.reset_index()
+
+    # Enrich with PV-related columns
+    if "pv" in component_types:
+        df[COLUMN_PV_PROD] = -df.get(COLUMN_PV_NEG, 0)
+        df[COLUMN_PV_EXCESS] = (df[COLUMN_PV_PROD] - df[COLUMN_CONSUMPTION]).clip(
+            lower=0
+        )
+
+        if "battery" in component_types:
+            df[COLUMN_PV_IN_BAT] = df[[COLUMN_PV_EXCESS, COLUMN_BATTERY_POS]].min(
+                axis=1
+            )
+        else:
+            df[COLUMN_PV_IN_BAT] = 0
+
+        df[COLUMN_PV_FEEDIN] = df[COLUMN_PV_EXCESS] - df[COLUMN_PV_IN_BAT]
+        df[COLUMN_PV_SELF] = (df[COLUMN_PV_PROD] - df[COLUMN_PV_EXCESS]).clip(lower=0)
+        df[COLUMN_PV_SHARE] = df[COLUMN_PV_SELF] / df[COLUMN_CONSUMPTION].replace(
+            0, pd.NA
+        )
+
+    # Convert timestamp to Berlin time
+    if df[COLUMN_TIMESTAMP].dt.tz is None:
+        df[COLUMN_TIMESTAMP] = df[COLUMN_TIMESTAMP].dt.tz_localize("UTC")
+    df[COLUMN_TIMESTAMP] = df[COLUMN_TIMESTAMP].dt.tz_convert(TZ_NAME)
+
+    # Basic renaming
+    rename_map: Dict[str, str] = {
+        COLUMN_TIMESTAMP: COLUMN_TIMESTAMP_NAMED,
+        COLUMN_GRID: COLUMN_GRID_NAMED,
+        COLUMN_CONSUMPTION: COLUMN_CONSUMPTION_NAMED,
+    }
+
+    if "battery" in component_types:
+        rename_map[COLUMN_BATTERY] = COLUMN_BATTERY_NAMED
+
+    if "pv" in component_types:
+        rename_map.update(
+            {
+                "pv": COLUMN_PV_THROUGHPUT,
+                COLUMN_PV_PROD: COLUMN_PV_PROD,
+                COLUMN_PV_SELF: COLUMN_PV_SELF,
+                COLUMN_PV_FEEDIN: COLUMN_PV_FEEDIN,
+                COLUMN_PV_SHARE: COLUMN_PV_SHARE,
+            }
+        )
+        if "battery" in component_types:
+            rename_map[COLUMN_PV_BAT] = COLUMN_PV_IN_BAT
+
+    # Rename individual component IDs
+    single_comp = [col for col in df.columns if col.isdigit()]
+
+    if "battery" in component_types:
+        battery_ids = {
+            str(i) for i in mcfg.component_type_ids(component_type="battery")
+        }
+        rename_map.update(
+            {col: f"Batterie #{col}" for col in single_comp if col in battery_ids}
+        )
+
+    if "pv" in component_types:
+        pv_ids = {str(i) for i in mcfg.component_type_ids(component_type="pv")}
+        rename_map.update({col: f"PV #{col}" for col in single_comp if col in pv_ids})
+
+    df_renamed = df.rename(columns=rename_map)
+
+    # Add derived net import column
+    df_renamed[COLUMN_NET_IMPORT] = df_renamed[COLUMN_GRID_NAMED].clip(lower=0)
+
+    # Select main columns for compact display
+    def _get_main_columns(
+        columns: Iterable[str], component_types: List[str]
+    ) -> List[str]:
+        base = {
+            COLUMN_TIMESTAMP_NAMED,
+            COLUMN_GRID_NAMED,
+            COLUMN_NET_IMPORT,
+            COLUMN_CONSUMPTION_NAMED,
+        }
+
+        if "battery" in component_types:
+            base.add(COLUMN_BATTERY_NAMED)
+
+        if "pv" in component_types:
+            base.update(
+                {
+                    COLUMN_PV_THROUGHPUT,
+                    COLUMN_PV_PROD,
+                    COLUMN_PV_SELF,
+                    COLUMN_PV_FEEDIN,
+                }
+            )
+            if "battery" in component_types:
+                base.update({COLUMN_PV_IN_BAT, COLUMN_PV_SHARE})
+
+        # Add individual component columns like "PV #1", "Batterie #3", etc.
+        base.update({col for col in columns if "#" in col})
+
+        return [col for col in columns if col in base]
+
+    main_df = df_renamed[_get_main_columns(df_renamed.columns, component_types)]
+
+    return main_df, df_renamed
+
+
+def compute_power_df(
+    main_df: pd.DataFrame, resolution: Union[str, pd.Timedelta]
+) -> pd.DataFrame:
+    """Compute energy mix (PV vs grid) and return a summary power DataFrame.
+
+    Args:
+        main_df: DataFrame with energy data, including 'Netzbezug'
+                 and optionally 'PV Eigenverbrauch'.
+        resolution: Time resolution of each row in the DataFrame (e.g., "15min").
+
+    Returns:
+        A DataFrame summarizing the energy source mix in kWh, %, and average kW.
+    """
+    resolution = pd.to_timedelta(resolution)
+    hours = resolution.total_seconds() / 3600
+
+    # Calculate energy from grid
+    grid_kwh = round(main_df[COLUMN_NET_IMPORT].sum() * hours, 2)
+
+    if COLUMN_PV_SELF in main_df.columns:
+        # Calculate energy from PV
+        pv_self_kwh = round(main_df[COLUMN_PV_SELF].sum() * hours, 2)
+        total_kwh = pv_self_kwh + grid_kwh
+
+        energy_kwh = [pv_self_kwh, grid_kwh]
+        energy_labels = ["PV", "Netz"]
+
+        return pd.DataFrame(
+            {
+                "Energiebezug": energy_labels,
+                "Energie [kWh]": energy_kwh,
+                "Energie %": [round(e / total_kwh * 100, 2) for e in energy_kwh],
+                "Energie [kW]": [round(e / hours, 2) for e in energy_kwh],
+            }
+        )
+
+    # Only grid consumption available
+    return pd.DataFrame(
+        {
+            "Energiebezug": ["Netz"],
+            "Energie [kWh]": [grid_kwh],
+            "Energie %": [100.0],
+            "Energie [kW]": [round(grid_kwh / hours, 2)],
+        }
+    )
+
+
+def print_pv_sums(main_df: pd.DataFrame, resolution: pd.Timedelta) -> None:
+    """Print formatted sums for each PV column.
+
+    Args:
+        main_df: DataFrame containing PV columns with energy data.
+        resolution: Time resolution of each row in the DataFrame (e.g., "15min").
+    """
+    pv_columns = [col for col in main_df.columns.tolist() if "PV #" in col]
+
+    for pv in pv_columns:
+        pv_sum = round(main_df[pv].sum() * resolution * -1, 2)
+        formatted_sum = (
+            f"{pv_sum:,.2f}".replace(",", "X").replace(".", ",").replace("X", ".")
+        )
+        print(f"{pv:<7}:   {formatted_sum} kWh")
+
+
+def create_pv_analysis_df(
+    main_df: pd.DataFrame,
+    pv_filter: List[str],
+    pvgrid_filter: str,
+    pv_grid_filter_options: List[str],
+) -> pd.DataFrame:
+    """Create a DataFrame for PV analysis based on selected filters.
+
+    Args:
+        main_df: DataFrame containing PV and grid data.
+        pv_filter: List of PV components to include (e.g., ["1", "2"] or ["Alle"]).
+        pvgrid_filter: Filter option for PV and grid analysis (e.g., "PV", "Grid", "PV + Grid").
+        pv_grid_filter_options: List of available filter options for PV and grid.
+    Returns:
+        A DataFrame with PV feed-in data, reshaped for analysis.
+    """
+    # Case 1: Only PV
+    if pvgrid_filter == pv_grid_filter_options[1]:
+        pv_columns = (
+            [col for col in main_df.columns if "PV #" in col]
+            if "Alle" in pv_filter
+            else [f"PV {pv}" for pv in pv_filter]
+        )
+        df = main_df[[COLUMN_TIMESTAMP_NAMED] + pv_columns].copy()
+        df = df.melt(
+            id_vars=[COLUMN_TIMESTAMP_NAMED],
+            value_vars=pv_columns,
+            var_name="PV",
+            value_name=COLUMN_PV_FEEDIN,
+        )
+        df[COLUMN_PV_FEEDIN] *= -1
+        df["PV"] = df["PV"].str[3:]
+
+    # Case 2: Only Grid
+    elif pvgrid_filter == pv_grid_filter_options[2]:
+        df = main_df[[COLUMN_TIMESTAMP_NAMED, COLUMN_GRID_NAMED]].copy()
+        df["PV"] = "#"
+
+    # Case 3: Grid + PV split
+    else:
+        pv_columns = (
+            [col for col in main_df.columns if "PV #" in col]
+            if "Alle" in pv_filter
+            else [f"PV {pv}" for pv in pv_filter]
+        )
+        df = main_df[[COLUMN_TIMESTAMP_NAMED, COLUMN_GRID_NAMED] + pv_columns].copy()
+        df = df.melt(
+            id_vars=[COLUMN_TIMESTAMP_NAMED, COLUMN_GRID_NAMED],
+            value_vars=pv_columns,
+            var_name="PV",
+            value_name=COLUMN_PV_FEEDIN,
+        )
+        df[COLUMN_GRID_NAMED] /= len(pv_columns)
+        df[COLUMN_PV_FEEDIN] *= -1
+        df["PV"] = df["PV"].str[3:]
+
+    return df
+
+
+def create_battery_analysis_df(
+    main_df: pd.DataFrame, bat_filter: List[str]
+) -> pd.DataFrame:
+    """Create a DataFrame for battery analysis based on selected filters.
+
+    Args:
+        main_df: DataFrame containing battery data.
+        bat_filter: List of battery components to include (e.g., ["1", "2"] or ["Alle"]).
+    Returns:
+        A DataFrame with battery throughput data, reshaped for analysis.
+    """
+    bat_columns = (
+        [col for col in main_df.columns if "Batterie #" in col]
+        if "Alle" in bat_filter
+        else [f"Batterie {i}" for i in bat_filter]
+    )
+
+    df = main_df[bat_columns].copy()
+    df[COLUMN_TIMESTAMP_NAMED] = main_df.index
+
+    df = df.melt(
+        id_vars=[COLUMN_TIMESTAMP_NAMED],
+        value_vars=bat_columns,
+        var_name="Batterie",
+        value_name=COLUMN_BATTERY_NAMED,
+    )
+    df["Batterie"] = df["Batterie"].str[9:]
+
+    return df