\n",
+ "
\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "5d84b2ad45b8481e87988d24d56d4be0",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 0,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "text-cell-h1",
+ "formattedRanges": []
+ },
+ "source": [
+ "# Intro"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "270a2dee7ada4f73b5519178c2db906b",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 1,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "text-cell-p",
+ "formattedRanges": []
+ },
+ "source": [
+ "In diesem Notebook können Sie die Daten aus der Reporting API analysieren und visualisieren. Sie haben die Möglichkeit, den gewünschten Zeitraum und die Auflösung der zu analysierenden Daten festzulegen.\n",
+ "Die Analyse umfasst wichtige Kennzahlen zur Stromgewinnung aus Photovoltaikanlagen (PV), zur Nutzung von Batteriespeichern sowie zum Netzanschluss. Die Ergebnisse werden in anschaulichen Charts dargestellt, um Ihnen einen klaren Überblick über die Energieflüsse zu ermöglichen.\n",
+ "Wählen Sie einfach Ihre gewünschten Parameter aus, um tiefere Einblicke in Ihre Energieerzeugung und -nutzung zu erhalten."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "7297b3f1eb444881bced1e88a1fe898d",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 3,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "text-cell-h1",
+ "formattedRanges": []
+ },
+ "source": [
+ "# Inputs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "cell_id": "6f41fc6501a144e4b39d218a4ee1f04a",
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 577,
+ "execution_start": 1758275930702,
+ "is_code_hidden": true,
+ "source_hash": "ee7004ba"
+ },
+ "outputs": [],
+ "source": [
+ "directory = \"/work\"\n",
+ "toml_files = [f for f in os.listdir(directory) if f.endswith(\".toml\")]\n",
+ "if not toml_files:\n",
+ " raise FileNotFoundError(\"No .toml files found in /work.\")\n",
+ "\n",
+ "configs: dict[str, \"MicrogridConfig\"] = {}\n",
+ "for toml_file in toml_files:\n",
+ " configs.update(MicrogridConfig.load_configs(toml_file))\n",
+ "available_microgrids = sorted(list(int(x) for x in configs.keys()))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "1452286565354ed48f8a1f5a598fa4b1",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 4,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "text-cell-p",
+ "formattedRanges": [
+ {
+ "fromCodePoint": 97,
+ "ranges": [],
+ "toCodePoint": 106,
+ "type": "link",
+ "url": "google.com"
+ },
+ {
+ "fromCodePoint": 152,
+ "ranges": [],
+ "toCodePoint": 165,
+ "type": "link",
+ "url": "https://kuiper.frequenz.com/"
+ },
+ {
+ "fromCodePoint": 481,
+ "ranges": [],
+ "toCodePoint": 485,
+ "type": "link",
+ "url": "google.com"
+ }
+ ]
+ },
+ "source": [
+ "Um das Notebook nutzen zu können müssen zunächst die API Anmeldeinformationen hinterlegt werden (Anleitung). Die API Anmeldeinformationen finden Sie im Kuiper-Portal. Dort finden Sie ebenfalls ihre Microgrid ID, die Meter PQ ID, die Meter Batterie IDs sowie die Meter PV IDs. Bitte geben Sie Ihre Komponenten IDs unten ein, wählen sie ein Start- und Enddatum, wählen sie eine Auflösung und klicken Sie auf Start, um das Notebook auszuführen.\n",
+ "Eine Anleitung zum Vorgehen finden Sie hier."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "allow_embed": false,
+ "cell_id": "b0c192cc84ba4bd2a4d69e0a7f834b33",
+ "deepnote_allow_multiple_values": false,
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 5,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "input-select",
+ "deepnote_input_label": "Microgrid Name",
+ "deepnote_variable_custom_options": [
+ "grid",
+ "consumption",
+ "battery",
+ "chp",
+ "ev",
+ "pv"
+ ],
+ "deepnote_variable_default_value": "",
+ "deepnote_variable_name": "microgrid_id",
+ "deepnote_variable_options": [
+ "1",
+ "3",
+ "4",
+ "5",
+ "6",
+ "7",
+ "8",
+ "9",
+ "10",
+ "11",
+ "13",
+ "36",
+ "66",
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+ "174",
+ "175",
+ "176",
+ "177",
+ "178",
+ "179",
+ "180",
+ "181",
+ "182",
+ "183",
+ "184",
+ "185",
+ "186",
+ "187",
+ "188",
+ "189",
+ "190",
+ "191",
+ "193",
+ "194",
+ "195",
+ "196",
+ "197",
+ "198",
+ "199",
+ "200",
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+ "202",
+ "203",
+ "204",
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+ "221",
+ "222",
+ "223",
+ "224",
+ "225",
+ "226",
+ "227",
+ "228",
+ "229",
+ "233",
+ "239"
+ ],
+ "deepnote_variable_select_type": "from-variable",
+ "deepnote_variable_selected_variable": "available_microgrids",
+ "deepnote_variable_value": "13",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275931332,
+ "source_hash": "b9c1d5fa"
+ },
+ "outputs": [],
+ "source": [
+ "microgrid_id = '13'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "f1f0a050ac414d34809621e7b0ed86bb",
+ "deepnote_app_block_visible": false,
+ "deepnote_cell_type": "text-cell-p",
+ "formattedRanges": []
+ },
+ "source": [
+ "Zeitrahmen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "cell_id": "976dd017eeb34145861b965741af3b6a",
+ "deepnote_app_block_group_id": "d4f39a494830424e97016c07754914c6",
+ "deepnote_app_block_order": 9,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "input-date",
+ "deepnote_input_date_version": 2,
+ "deepnote_input_label": "Start Datum (yyyy-mm-dd)",
+ "deepnote_variable_name": "start_date",
+ "deepnote_variable_value": "2025-08-01T00:00:00.000Z",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275931382,
+ "source_hash": "6ef890cf"
+ },
+ "outputs": [],
+ "source": [
+ "\n",
+ "from dateutil.parser import parse as _deepnote_parse\n",
+ "start_date = _deepnote_parse('2025-08-01T00:00:00.000Z').date()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "cell_id": "391f9ece0acd4b9d828752d6149327ff",
+ "deepnote_app_block_group_id": "d4f39a494830424e97016c07754914c6",
+ "deepnote_app_block_order": 10,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "input-date",
+ "deepnote_input_date_version": 2,
+ "deepnote_input_label": "Enddatum (yyyy-mm-dd)",
+ "deepnote_variable_name": "end_date",
+ "deepnote_variable_value": "2025-08-31T00:00:00.000Z",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 1,
+ "execution_start": 1758275931431,
+ "source_hash": "c872cec1"
+ },
+ "outputs": [],
+ "source": [
+ "\n",
+ "from dateutil.parser import parse as _deepnote_parse\n",
+ "end_date = _deepnote_parse('2025-08-31T00:00:00.000Z').date()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "cell_id": "3223472cff8d43db9a0d766a4b6a409c",
+ "deepnote_app_block_group_id": "d4f39a494830424e97016c07754914c6",
+ "deepnote_app_block_order": 11,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "input-text",
+ "deepnote_input_label": "Auflösung in Minuten (Standard: 15min)",
+ "deepnote_variable_name": "resolution",
+ "deepnote_variable_value": "15",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275931482,
+ "source_hash": "3440d099"
+ },
+ "outputs": [],
+ "source": [
+ "resolution = '15'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "abe29461342a43e2ab37ca2280edbb02",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 12,
+ "deepnote_app_block_visible": true,
+ "deepnote_button_behavior": "set_variable",
+ "deepnote_button_color_scheme": "blue",
+ "deepnote_button_title": "Start",
+ "deepnote_cell_type": "button",
+ "deepnote_variable_name": "run_notebook",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275931532,
+ "source_hash": "ec0a32f1"
+ },
+ "source": [
+ "run_notebook = False"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "cell_id": "5785e11d28af42839b3ab165a0513a0c",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 13,
+ "deepnote_app_block_visible": true,
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_app_is_output_hidden": false,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275931592,
+ "is_code_hidden": true,
+ "source_hash": "e875544e"
+ },
+ "outputs": [],
+ "source": [
+ "# Casting input values\n",
+ "def cast_input(input_string, output_type=int):\n",
+ " if input_string == 'microgrid_id':\n",
+ " try :\n",
+ " globals()[f'{input_string}'] = int(globals()[f'{input_string}'])\n",
+ " # Check if valid microgrid id\n",
+ " except:\n",
+ " globals()[f'{input_string}'] = 0\n",
+ " Exception(input_string + ' muss ganzzahlig sein')\n",
+ " if input_string == 'resolution':\n",
+ " try:\n",
+ " current_val = globals()[input_string]\n",
+ "\n",
+ " # Only convert if it's not already a timedelta\n",
+ " if not isinstance(current_val, timedelta):\n",
+ " globals()[input_string] = timedelta(seconds=int(current_val) * 60)\n",
+ "\n",
+ " except Exception:\n",
+ " globals()[input_string] = timedelta(seconds=0)\n",
+ " raise Exception(input_string + ' muss ganzzahlig sein')\n",
+ " elif output_type == int:\n",
+ " try:\n",
+ " globals()[f'{input_string}'] = int(globals()[f'{input_string}'])\n",
+ " except:\n",
+ " globals()[f'{input_string}'] = 0\n",
+ " raise Exception(input_string + ' muss ganzzahlig sein')\n",
+ "\n",
+ " elif output_type == list: # Added list\n",
+ " try:\n",
+ " strings = globals()[f'{input_string}'].split(',')\n",
+ " globals()[f'{input_string}'] = [int(x) for x in strings]\n",
+ " except Exception as e:\n",
+ " raise Exception(f'Prüfen Sie die {input_string}: {str(e)}')\n",
+ "\n",
+ " elif output_type == int:\n",
+ " try:\n",
+ " globals()[f'{input_string}'] = int(globals()[f'{input_string}'])\n",
+ " except:\n",
+ " globals()[f'{input_string}'] = 0\n",
+ " raise Exception(input_string + ' muss ganzzahlig sein')\n",
+ "\n",
+ " elif output_type == date:\n",
+ " try:\n",
+ " dt = globals()[f'{input_string}']\n",
+ " if input_string == 'start_date':\n",
+ " dt = datetime(dt.year, dt.month, dt.day, 0, 0, 0, tzinfo=ZoneInfo(\"CET\"))\n",
+ " else:\n",
+ " dt = datetime(dt.year, dt.month, dt.day, 0, 0, 0, tzinfo=ZoneInfo(\"CET\"))\n",
+ " globals()[f'{input_string}'] = dt.astimezone(ZoneInfo('UTC'))\n",
+ " except:\n",
+ " raise Exception(input_string + ' muss ein Datumsformat sein')\n",
+ "\n",
+ "if run_notebook:\n",
+ " mapper = ColumnMapper.from_yaml(\"schema_mapping.yaml\")\n",
+ " variable_types = {\n",
+ " 'start_date': date,\n",
+ " 'end_date': date,\n",
+ " 'microgrid_id': int,\n",
+ " 'resolution': timedelta\n",
+ " }\n",
+ "\n",
+ " for input in variable_types:\n",
+ " if globals()[input]:\n",
+ " cast_input(input, variable_types[input])\n",
+ "\n",
+ " if start_date >= end_date:\n",
+ " raise Exception('Prüfen Sie die eingegebenen Daten. Das Startdatum muss kleiner oder gleich dem Enddatum sein.')\n",
+ " else:\n",
+ " start_dt = start_date\n",
+ " end_dt = end_date"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "5a2f1e7b832446d89c4f0ca928a2ddf7",
+ "deepnote_app_block_visible": false,
+ "deepnote_cell_type": "text-cell-h2",
+ "formattedRanges": []
+ },
+ "source": [
+ "## Datenimport"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "cell_id": "56302f513aee43c9b1e77a105692a762",
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275931652,
+ "is_code_hidden": true,
+ "source_hash": "4cc54536"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.deepnote.dataframe.v3+json": {
+ "column_count": 0,
+ "columns": [],
+ "preview_row_count": 0,
+ "row_count": 0,
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+ },
+ "text/html": [
+ "🇩🇪 Deutsche Anleitung (Klicken zum Anzeigen)
\n", + "\n", + "🛠️ Anleitung
\n", + "Das Notebook benötigt einige Einstellungen, bevor es zum ersten Mal ausgeführt werden kann.
\n", + "\n", + "- \n",
+ "
- ✅ Neueste Paketversion sicherstellen: Es wird empfohlen, die neuesten Version der benötigten Pakete zu installieren. Alle Versionen finden Sie hier: frequenz-lib-notebooks, frequenz-client-reporting. Ersetzen Sie bei der Installation die vordefinierte Version durch die gewünschte. \n", + "
- 🛠️ Führen Sie die erste Code-Zelle aus: Klicken Sie auf das blaue Dreieck, um Pakete zu installieren. Danach verschieben Sie sie zu \"requirements.txt\" (weitere Informationen finden Sie im Dokument \"Anleitung: Deepnote Projekt erstellen und Notebook importieren\", Abschnitt \"5. Requirements installieren\"). \n", + "
- 📦 Importieren Sie die Bibliotheken: Führen Sie die nächsten zwei Code-Zellen aus, um die notwendigen Bibliotheken zu importieren und die Datei
microgrids.tomlzu laden. ⏳ Warten Sie, bis die Ausführung abgeschlossen ist. \n",
+ " - 🎯 Konfigurieren Sie die Eingaben: Füllen Sie die erforderlichen Felder unter \"Input\" aus. \n", + "
- 🚀 Notebook ausführen: Klicken Sie auf den \"Start\"-Button, sobald alle Eingaben ausgefüllt sind. \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "Empty DataFrame\n",
+ "Columns: []\n",
+ "Index: []"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "if run_notebook:\n",
+ " toml_path = os.path.join(directory, f'microgrids_eid{configs[str(microgrid_id)]._metadata.gid}.toml')\n",
+ "\n",
+ " mdata = component_data.MicrogridData(\n",
+ " server_url=os.environ[\"REPORTING_SERVER_URL\"],\n",
+ " auth_key=os.environ[\"REPORTING_API_KEY\"],\n",
+ " sign_secret=os.environ[\"REPORTING_API_SECRET\"],\n",
+ " microgrid_config_path=toml_path,\n",
+ " ) \n",
+ "\n",
+ " mids = [i.replace(\"iot\", \"\") for i in mdata.microgrid_ids] \n",
+ "\n",
+ " mcfg = mdata.microgrid_configs[str(microgrid_id)]\n",
+ " ctypes = mcfg.component_types()\n",
+ " component_types = ctypes\n",
+ "\n",
+ " df = await mdata.ac_active_power(\n",
+ " microgrid_id=microgrid_id, \n",
+ " component_types=component_types,\n",
+ " start=start_date,\n",
+ " end=end_date,\n",
+ " resampling_period=resolution,\n",
+ " keep_components=True,\n",
+ " splits=True,\n",
+ " )\n",
+ "\n",
+ " print(f\"Received {df.shape[0]} rows and {df.shape[1]} columns\")\n",
+ " # df.to_csv('raw_df.csv')\n",
+ "\n",
+ "else:\n",
+ " df = pd.DataFrame()\n",
+ "\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "9d90c66811b8427aa5f2dee24a00e729",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 16,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "text-cell-h1",
+ "formattedRanges": []
+ },
+ "source": [
+ "# Output"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "cell_id": "993a8e57489b4957856228d6796b98e7",
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275931712,
+ "is_code_hidden": true,
+ "source_hash": "d8859aec"
+ },
+ "outputs": [],
+ "source": [
+ "if run_notebook:\n",
+ " df = _add_pv_energy_flows(df)\n",
+ " df = df.reset_index()\n",
+ " df = mapper.to_canonical(df)\n",
+ "\n",
+ " energy_report_df = create_energy_report_dfs(df, component_types, mcfg)\n",
+ "\n",
+ " # base_cols = ['Zeitpunkt', 'Netzbezug', 'Netto Gesamtverbrauch']\n",
+ " base_cols = ['timestamp', 'net_import', 'net_consumption']\n",
+ " optional_cols = {\n",
+ " # 'pv': ['PV Produktion', 'PV Einspeisung'],\n",
+ " # 'battery': ['Batterie Durchsatz'],\n",
+ " 'pv': ['pv_prod', 'pv_feedin'],\n",
+ " 'battery': ['battery_throughput'],\n",
+ "\n",
+ " }\n",
+ "\n",
+ " # Start with base columns\n",
+ " cols = base_cols[:]\n",
+ "\n",
+ " # Add optional columns if the component is present\n",
+ " for comp, comp_cols in optional_cols.items():\n",
+ " if comp in component_types:\n",
+ " cols.extend(comp_cols)\n",
+ "\n",
+ " overview_df = energy_report_df[cols]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "cell_id": "6af522afeeee4cb1aa3cb90201dec110",
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275931782,
+ "is_code_hidden": true,
+ "source_hash": "ec500581"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.deepnote.dataframe.v3+json": {
+ "column_count": 0,
+ "columns": [],
+ "preview_row_count": 0,
+ "row_count": 0,
+ "rows": [],
+ "type": "dataframe"
+ },
+ "text/html": [
+ "| \n", + " |
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+ ],
+ "source": [
+ "if run_notebook:\n",
+ " grid_consumption_sum = (energy_report_df.get(\"net_import\", 0) * (resolution.seconds / 3600)).sum()\n",
+ " energy_summary_df = compute_energy_summary(energy_report_df, resolution=resolution)\n",
+ " energy_metrics_dict = aggregate_pv_metrics(energy_report_df=energy_report_df, resolution=resolution, grid_consumption_sum=grid_consumption_sum)\n",
+ "\n",
+ " (pv_feed_in_sum,\n",
+ " pv_production_sum,\n",
+ " pv_self_consumption_sum,\n",
+ " pv_bat_sum,\n",
+ " pv_self_consumption_share,\n",
+ " pv_total_consumption_share,\n",
+ " net_site_consumption_sum,\n",
+ " peak,\n",
+ " peak_date) = aggregate_pv_metrics(energy_report_df, resolution, grid_consumption_sum).values()\n",
+ "\n",
+ "else:\n",
+ " energy_summary_df = pd.DataFrame()\n",
+ "\n",
+ "energy_summary_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "a0595ecdadae4c11accf52717904faa7",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 20,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "text-cell-h2",
+ "formattedRanges": [],
+ "is_collapsed": false
+ },
+ "source": [
+ "## Übersicht"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "cell_id": "257b9b778483402ea47d8808668c0fa6",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 21,
+ "deepnote_app_block_visible": true,
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_app_is_output_hidden": false,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275931842,
+ "is_code_hidden": true,
+ "source_hash": "f4691930"
+ },
+ "outputs": [],
+ "source": [
+ "if run_notebook:\n",
+ " print(f'{datetime.strftime(start_date.astimezone(ZoneInfo(\"CET\")).date(), \"%d.%m.%Y\")} - {datetime.strftime(end_date.astimezone(ZoneInfo(\"CET\")).date(), \"%d.%m.%Y\")}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "cell_id": "43d48c5ef7bf47ef8f0b3476d233a89d",
+ "deepnote_app_block_group_id": "63eefa7b8b0249eeb99f3cb3fc6d385e",
+ "deepnote_app_block_order": 23,
+ "deepnote_app_block_visible": true,
+ "deepnote_big_number_comparison_enabled": false,
+ "deepnote_big_number_comparison_format": "",
+ "deepnote_big_number_comparison_title": "",
+ "deepnote_big_number_comparison_type": "",
+ "deepnote_big_number_comparison_value": "",
+ "deepnote_big_number_format": "number",
+ "deepnote_big_number_title": "Brutto Stromverbrauch kWh",
+ "deepnote_big_number_value": "net_site_consumption_sum",
+ "deepnote_cell_type": "big-number",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275931912,
+ "source_hash": "763a5c17"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"comparisonTitle\": \"\", \"comparisonValue\": \"\", \"title\": \"Brutto Stromverbrauch kWh\", \"value\": \"0\"}'"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "def __deepnote_big_number__():\n",
+ " import json\n",
+ " import jinja2\n",
+ " from jinja2 import meta\n",
+ "\n",
+ " def render_template(template):\n",
+ " parsed_content = jinja2.Environment().parse(template)\n",
+ "\n",
+ " required_variables = meta.find_undeclared_variables(parsed_content)\n",
+ "\n",
+ " context = {\n",
+ " variable_name: globals().get(variable_name)\n",
+ " for variable_name in required_variables\n",
+ " }\n",
+ "\n",
+ " result = jinja2.Environment().from_string(template).render(context)\n",
+ "\n",
+ " return result\n",
+ "\n",
+ " rendered_title = render_template(\"Brutto Stromverbrauch kWh\")\n",
+ " rendered_comparison_title = render_template(\"\")\n",
+ "\n",
+ " return json.dumps({\n",
+ " \"comparisonTitle\": rendered_comparison_title,\n",
+ " \"comparisonValue\": \"\",\n",
+ " \"title\": rendered_title,\n",
+ " \"value\": f\"{net_site_consumption_sum}\"\n",
+ " })\n",
+ "\n",
+ "__deepnote_big_number__()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "cell_id": "11b6e22ab1ae4da4b4092d7efb8740b7",
+ "deepnote_app_block_group_id": "63eefa7b8b0249eeb99f3cb3fc6d385e",
+ "deepnote_app_block_order": 24,
+ "deepnote_app_block_visible": true,
+ "deepnote_big_number_comparison_enabled": false,
+ "deepnote_big_number_comparison_format": "",
+ "deepnote_big_number_comparison_title": "",
+ "deepnote_big_number_comparison_type": "",
+ "deepnote_big_number_comparison_value": "",
+ "deepnote_big_number_format": "number",
+ "deepnote_big_number_title": "Bezug in kWh",
+ "deepnote_big_number_value": "grid_consumption_sum",
+ "deepnote_cell_type": "big-number",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275931972,
+ "source_hash": "1c6e87d9"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"comparisonTitle\": \"\", \"comparisonValue\": \"\", \"title\": \"Bezug in kWh\", \"value\": \"0\"}'"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "def __deepnote_big_number__():\n",
+ " import json\n",
+ " import jinja2\n",
+ " from jinja2 import meta\n",
+ "\n",
+ " def render_template(template):\n",
+ " parsed_content = jinja2.Environment().parse(template)\n",
+ "\n",
+ " required_variables = meta.find_undeclared_variables(parsed_content)\n",
+ "\n",
+ " context = {\n",
+ " variable_name: globals().get(variable_name)\n",
+ " for variable_name in required_variables\n",
+ " }\n",
+ "\n",
+ " result = jinja2.Environment().from_string(template).render(context)\n",
+ "\n",
+ " return result\n",
+ "\n",
+ " rendered_title = render_template(\"Bezug in kWh\")\n",
+ " rendered_comparison_title = render_template(\"\")\n",
+ "\n",
+ " return json.dumps({\n",
+ " \"comparisonTitle\": rendered_comparison_title,\n",
+ " \"comparisonValue\": \"\",\n",
+ " \"title\": rendered_title,\n",
+ " \"value\": f\"{grid_consumption_sum}\"\n",
+ " })\n",
+ "\n",
+ "__deepnote_big_number__()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "allow_embed": false,
+ "cell_id": "52f0a28347bf4b188ea395fea0258f9d",
+ "deepnote_app_block_visible": true,
+ "deepnote_big_number_comparison_enabled": false,
+ "deepnote_big_number_comparison_format": "",
+ "deepnote_big_number_comparison_title": "",
+ "deepnote_big_number_comparison_type": "",
+ "deepnote_big_number_comparison_value": "",
+ "deepnote_big_number_format": "number",
+ "deepnote_big_number_title": "PV Eigenverbrauch in kWh",
+ "deepnote_big_number_value": "pv_self_consumption_sum",
+ "deepnote_cell_type": "big-number",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275932032,
+ "source_hash": "24149a25"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"comparisonTitle\": \"\", \"comparisonValue\": \"\", \"title\": \"PV Eigenverbrauch in kWh\", \"value\": \"0\"}'"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "def __deepnote_big_number__():\n",
+ " import json\n",
+ " import jinja2\n",
+ " from jinja2 import meta\n",
+ "\n",
+ " def render_template(template):\n",
+ " parsed_content = jinja2.Environment().parse(template)\n",
+ "\n",
+ " required_variables = meta.find_undeclared_variables(parsed_content)\n",
+ "\n",
+ " context = {\n",
+ " variable_name: globals().get(variable_name)\n",
+ " for variable_name in required_variables\n",
+ " }\n",
+ "\n",
+ " result = jinja2.Environment().from_string(template).render(context)\n",
+ "\n",
+ " return result\n",
+ "\n",
+ " rendered_title = render_template(\"PV Eigenverbrauch in kWh\")\n",
+ " rendered_comparison_title = render_template(\"\")\n",
+ "\n",
+ " return json.dumps({\n",
+ " \"comparisonTitle\": rendered_comparison_title,\n",
+ " \"comparisonValue\": \"\",\n",
+ " \"title\": rendered_title,\n",
+ " \"value\": f\"{pv_self_consumption_sum}\"\n",
+ " })\n",
+ "\n",
+ "__deepnote_big_number__()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "cell_id": "2804afd629924243a4ec97789142e33a",
+ "deepnote_app_block_group_id": "63eefa7b8b0249eeb99f3cb3fc6d385e",
+ "deepnote_app_block_order": 22,
+ "deepnote_app_block_visible": true,
+ "deepnote_big_number_comparison_enabled": false,
+ "deepnote_big_number_comparison_format": "",
+ "deepnote_big_number_comparison_title": "",
+ "deepnote_big_number_comparison_type": "",
+ "deepnote_big_number_comparison_value": "",
+ "deepnote_big_number_format": "number",
+ "deepnote_big_number_title": "PV Gesamterzeugung in kWh",
+ "deepnote_big_number_value": "pv_production_sum",
+ "deepnote_cell_type": "big-number",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275932082,
+ "source_hash": "74535792"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"comparisonTitle\": \"\", \"comparisonValue\": \"\", \"title\": \"PV Gesamterzeugung in kWh\", \"value\": \"0\"}'"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "def __deepnote_big_number__():\n",
+ " import json\n",
+ " import jinja2\n",
+ " from jinja2 import meta\n",
+ "\n",
+ " def render_template(template):\n",
+ " parsed_content = jinja2.Environment().parse(template)\n",
+ "\n",
+ " required_variables = meta.find_undeclared_variables(parsed_content)\n",
+ "\n",
+ " context = {\n",
+ " variable_name: globals().get(variable_name)\n",
+ " for variable_name in required_variables\n",
+ " }\n",
+ "\n",
+ " result = jinja2.Environment().from_string(template).render(context)\n",
+ "\n",
+ " return result\n",
+ "\n",
+ " rendered_title = render_template(\"PV Gesamterzeugung in kWh\")\n",
+ " rendered_comparison_title = render_template(\"\")\n",
+ "\n",
+ " return json.dumps({\n",
+ " \"comparisonTitle\": rendered_comparison_title,\n",
+ " \"comparisonValue\": \"\",\n",
+ " \"title\": rendered_title,\n",
+ " \"value\": f\"{pv_production_sum}\"\n",
+ " })\n",
+ "\n",
+ "__deepnote_big_number__()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "cell_id": "c2dff4f16e3a4fd8bf74dc231380933a",
+ "deepnote_app_block_group_id": "63eefa7b8b0249eeb99f3cb3fc6d385e",
+ "deepnote_app_block_order": 25,
+ "deepnote_app_block_visible": true,
+ "deepnote_big_number_comparison_enabled": false,
+ "deepnote_big_number_comparison_format": "",
+ "deepnote_big_number_comparison_title": "",
+ "deepnote_big_number_comparison_type": "",
+ "deepnote_big_number_comparison_value": "",
+ "deepnote_big_number_format": "number",
+ "deepnote_big_number_title": "Einspeisung in kWh",
+ "deepnote_big_number_value": "pv_feed_in_sum",
+ "deepnote_cell_type": "big-number",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275932142,
+ "source_hash": "f8110522"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"comparisonTitle\": \"\", \"comparisonValue\": \"\", \"title\": \"Einspeisung in kWh\", \"value\": \"0\"}'"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "def __deepnote_big_number__():\n",
+ " import json\n",
+ " import jinja2\n",
+ " from jinja2 import meta\n",
+ "\n",
+ " def render_template(template):\n",
+ " parsed_content = jinja2.Environment().parse(template)\n",
+ "\n",
+ " required_variables = meta.find_undeclared_variables(parsed_content)\n",
+ "\n",
+ " context = {\n",
+ " variable_name: globals().get(variable_name)\n",
+ " for variable_name in required_variables\n",
+ " }\n",
+ "\n",
+ " result = jinja2.Environment().from_string(template).render(context)\n",
+ "\n",
+ " return result\n",
+ "\n",
+ " rendered_title = render_template(\"Einspeisung in kWh\")\n",
+ " rendered_comparison_title = render_template(\"\")\n",
+ "\n",
+ " return json.dumps({\n",
+ " \"comparisonTitle\": rendered_comparison_title,\n",
+ " \"comparisonValue\": \"\",\n",
+ " \"title\": rendered_title,\n",
+ " \"value\": f\"{pv_feed_in_sum}\"\n",
+ " })\n",
+ "\n",
+ "__deepnote_big_number__()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "cell_id": "6a34c92f919146f39cd78db950a45396",
+ "deepnote_app_block_group_id": "17547582a08344dd874013ef7e8d34eb",
+ "deepnote_app_block_order": 26,
+ "deepnote_app_block_visible": true,
+ "deepnote_big_number_comparison_enabled": false,
+ "deepnote_big_number_comparison_format": "",
+ "deepnote_big_number_comparison_title": "",
+ "deepnote_big_number_comparison_type": "",
+ "deepnote_big_number_comparison_value": "",
+ "deepnote_big_number_format": "number",
+ "deepnote_big_number_title": "Lastspitze in kW {{peak_date}}",
+ "deepnote_big_number_value": "peak",
+ "deepnote_cell_type": "big-number",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275932212,
+ "source_hash": "ad1dac55"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"comparisonTitle\": \"\", \"comparisonValue\": \"\", \"title\": \"Lastspitze in kW None\", \"value\": \"0\"}'"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "def __deepnote_big_number__():\n",
+ " import json\n",
+ " import jinja2\n",
+ " from jinja2 import meta\n",
+ "\n",
+ " def render_template(template):\n",
+ " parsed_content = jinja2.Environment().parse(template)\n",
+ "\n",
+ " required_variables = meta.find_undeclared_variables(parsed_content)\n",
+ "\n",
+ " context = {\n",
+ " variable_name: globals().get(variable_name)\n",
+ " for variable_name in required_variables\n",
+ " }\n",
+ "\n",
+ " result = jinja2.Environment().from_string(template).render(context)\n",
+ "\n",
+ " return result\n",
+ "\n",
+ " rendered_title = render_template(\"Lastspitze in kW {{peak_date}}\")\n",
+ " rendered_comparison_title = render_template(\"\")\n",
+ "\n",
+ " return json.dumps({\n",
+ " \"comparisonTitle\": rendered_comparison_title,\n",
+ " \"comparisonValue\": \"\",\n",
+ " \"title\": rendered_title,\n",
+ " \"value\": f\"{peak}\"\n",
+ " })\n",
+ "\n",
+ "__deepnote_big_number__()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "38f67458f12e4fe2bb73ee3aacf945a2",
+ "deepnote_app_block_group_id": "17547582a08344dd874013ef7e8d34eb",
+ "deepnote_app_block_order": 28,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "text-cell-p",
+ "formattedRanges": []
+ },
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "ca2ebe3b8b2248b0ad8212beb457f499",
+ "deepnote_app_block_group_id": "17547582a08344dd874013ef7e8d34eb",
+ "deepnote_app_block_order": 27,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "text-cell-p",
+ "formattedRanges": []
+ },
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "902aac301a7a45e9a7cad7b85d64eabc",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 31,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "text-cell-p",
+ "formattedRanges": [
+ {
+ "fromCodePoint": 0,
+ "marks": {
+ "bold": true
+ },
+ "toCodePoint": 32,
+ "type": "marks"
+ }
+ ]
+ },
+ "source": [
+ "Lastgang im zeitlichen Verlauf: \n",
+ "Hier werden die PV-Gesamtleistung, der Verbrauch (Last), der Netzbezug, die (PV) Einspeisung und die Batterie-Gesamtleistung im ausgewählten Zeitraum dargestellt."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "cell_id": "cfea1dcfd8014df3a5b4d22aef90f632",
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 1,
+ "execution_start": 1758275932261,
+ "is_code_hidden": true,
+ "source_hash": "42f2c682"
+ },
+ "outputs": [],
+ "source": [
+ "if run_notebook:\n",
+ " overview_df = mapper.to_display(overview_df)\n",
+ " fig = plot_time_series(overview_df, time_col=\"Zeitpunkt\", cols=None, title=\"Lastgang Übersicht\")\n",
+ " fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "303ba6d74b12493595de283e5ecc0e57",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 36,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "text-cell-p",
+ "formattedRanges": [
+ {
+ "fromCodePoint": 0,
+ "marks": {
+ "bold": true
+ },
+ "toCodePoint": 12,
+ "type": "marks"
+ }
+ ]
+ },
+ "source": [
+ "Energiebezug\n",
+ "Hier sehen Sie das Verhältnis von Eigenverbrauch und Netzbezug in dem von Ihnen gewählten Zeitraum an."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "cell_id": "ac107001d49d43c59f912f3058c4eea5",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 38,
+ "deepnote_app_block_visible": true,
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_app_is_output_hidden": false,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 1,
+ "execution_start": 1758275932311,
+ "is_code_hidden": true,
+ "source_hash": "c131d14e"
+ },
+ "outputs": [],
+ "source": [
+ "if run_notebook:\n",
+ " energy_summary_df = energy_summary_df.rename(columns={\"Energy Source\": \"Energiebezug\", \"Energy [kWh]\": \"Energie [kWh]\"})\n",
+ " pl.plot_energy_pie_chart(energy_summary_df)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "32e429c1767c4afaa148565792d08b1f",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 39,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "text-cell-h2",
+ "formattedRanges": [],
+ "is_collapsed": false
+ },
+ "source": [
+ "## PV"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "cell_id": "faa99db587294e108ebff11e49a868ca",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 40,
+ "deepnote_app_block_visible": true,
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_app_is_output_hidden": false,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 1,
+ "execution_start": 1758275932371,
+ "is_code_hidden": true,
+ "source_hash": "f4691930"
+ },
+ "outputs": [],
+ "source": [
+ "if run_notebook:\n",
+ " print(f'{datetime.strftime(start_date.astimezone(ZoneInfo(\"CET\")).date(), \"%d.%m.%Y\")} - {datetime.strftime(end_date.astimezone(ZoneInfo(\"CET\")).date(), \"%d.%m.%Y\")}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "cell_id": "6c76fc3d902044368bb8ac62106f5cd1",
+ "deepnote_app_block_group_id": "77790c88af014d8e95f12134a403cb13",
+ "deepnote_app_block_order": 41,
+ "deepnote_app_block_visible": true,
+ "deepnote_big_number_comparison_enabled": false,
+ "deepnote_big_number_comparison_format": "",
+ "deepnote_big_number_comparison_title": "",
+ "deepnote_big_number_comparison_type": "",
+ "deepnote_big_number_comparison_value": "",
+ "deepnote_big_number_format": "number",
+ "deepnote_big_number_title": "PV Gesamterzeugung in kWh",
+ "deepnote_big_number_value": "pv_production_sum",
+ "deepnote_cell_type": "big-number",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275932422,
+ "source_hash": "74535792"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"comparisonTitle\": \"\", \"comparisonValue\": \"\", \"title\": \"PV Gesamterzeugung in kWh\", \"value\": \"0\"}'"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "def __deepnote_big_number__():\n",
+ " import json\n",
+ " import jinja2\n",
+ " from jinja2 import meta\n",
+ "\n",
+ " def render_template(template):\n",
+ " parsed_content = jinja2.Environment().parse(template)\n",
+ "\n",
+ " required_variables = meta.find_undeclared_variables(parsed_content)\n",
+ "\n",
+ " context = {\n",
+ " variable_name: globals().get(variable_name)\n",
+ " for variable_name in required_variables\n",
+ " }\n",
+ "\n",
+ " result = jinja2.Environment().from_string(template).render(context)\n",
+ "\n",
+ " return result\n",
+ "\n",
+ " rendered_title = render_template(\"PV Gesamterzeugung in kWh\")\n",
+ " rendered_comparison_title = render_template(\"\")\n",
+ "\n",
+ " return json.dumps({\n",
+ " \"comparisonTitle\": rendered_comparison_title,\n",
+ " \"comparisonValue\": \"\",\n",
+ " \"title\": rendered_title,\n",
+ " \"value\": f\"{pv_production_sum}\"\n",
+ " })\n",
+ "\n",
+ "__deepnote_big_number__()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "cell_id": "ff2600513d724190a2cdd0a7e82656ef",
+ "deepnote_app_block_group_id": "77790c88af014d8e95f12134a403cb13",
+ "deepnote_app_block_order": 42,
+ "deepnote_app_block_visible": true,
+ "deepnote_big_number_comparison_enabled": false,
+ "deepnote_big_number_comparison_format": "",
+ "deepnote_big_number_comparison_title": "",
+ "deepnote_big_number_comparison_type": "",
+ "deepnote_big_number_comparison_value": "",
+ "deepnote_big_number_format": "number",
+ "deepnote_big_number_title": "PV Eigenverbrauch in kWh",
+ "deepnote_big_number_value": "pv_self_consumption_sum",
+ "deepnote_cell_type": "big-number",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 1,
+ "execution_start": 1758275932481,
+ "source_hash": "24149a25"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"comparisonTitle\": \"\", \"comparisonValue\": \"\", \"title\": \"PV Eigenverbrauch in kWh\", \"value\": \"0\"}'"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "def __deepnote_big_number__():\n",
+ " import json\n",
+ " import jinja2\n",
+ " from jinja2 import meta\n",
+ "\n",
+ " def render_template(template):\n",
+ " parsed_content = jinja2.Environment().parse(template)\n",
+ "\n",
+ " required_variables = meta.find_undeclared_variables(parsed_content)\n",
+ "\n",
+ " context = {\n",
+ " variable_name: globals().get(variable_name)\n",
+ " for variable_name in required_variables\n",
+ " }\n",
+ "\n",
+ " result = jinja2.Environment().from_string(template).render(context)\n",
+ "\n",
+ " return result\n",
+ "\n",
+ " rendered_title = render_template(\"PV Eigenverbrauch in kWh\")\n",
+ " rendered_comparison_title = render_template(\"\")\n",
+ "\n",
+ " return json.dumps({\n",
+ " \"comparisonTitle\": rendered_comparison_title,\n",
+ " \"comparisonValue\": \"\",\n",
+ " \"title\": rendered_title,\n",
+ " \"value\": f\"{pv_self_consumption_sum}\"\n",
+ " })\n",
+ "\n",
+ "__deepnote_big_number__()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "cell_id": "d3b4783e50734053857ebb807fefd3a2",
+ "deepnote_app_block_group_id": "77790c88af014d8e95f12134a403cb13",
+ "deepnote_app_block_order": 43,
+ "deepnote_app_block_visible": true,
+ "deepnote_big_number_comparison_enabled": false,
+ "deepnote_big_number_comparison_format": "",
+ "deepnote_big_number_comparison_title": "",
+ "deepnote_big_number_comparison_type": "",
+ "deepnote_big_number_comparison_value": "",
+ "deepnote_big_number_format": "percent",
+ "deepnote_big_number_title": "PV Eigenverbrauch in %",
+ "deepnote_big_number_value": "pv_self_consumption_share",
+ "deepnote_cell_type": "big-number",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275932532,
+ "source_hash": "35efbce9"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"comparisonTitle\": \"\", \"comparisonValue\": \"\", \"title\": \"PV Eigenverbrauch in %\", \"value\": \"0\"}'"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "def __deepnote_big_number__():\n",
+ " import json\n",
+ " import jinja2\n",
+ " from jinja2 import meta\n",
+ "\n",
+ " def render_template(template):\n",
+ " parsed_content = jinja2.Environment().parse(template)\n",
+ "\n",
+ " required_variables = meta.find_undeclared_variables(parsed_content)\n",
+ "\n",
+ " context = {\n",
+ " variable_name: globals().get(variable_name)\n",
+ " for variable_name in required_variables\n",
+ " }\n",
+ "\n",
+ " result = jinja2.Environment().from_string(template).render(context)\n",
+ "\n",
+ " return result\n",
+ "\n",
+ " rendered_title = render_template(\"PV Eigenverbrauch in %\")\n",
+ " rendered_comparison_title = render_template(\"\")\n",
+ "\n",
+ " return json.dumps({\n",
+ " \"comparisonTitle\": rendered_comparison_title,\n",
+ " \"comparisonValue\": \"\",\n",
+ " \"title\": rendered_title,\n",
+ " \"value\": f\"{pv_self_consumption_share}\"\n",
+ " })\n",
+ "\n",
+ "__deepnote_big_number__()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "cell_id": "b31d2458f8b64dcf8f0a3ea5511a77e0",
+ "deepnote_app_block_group_id": "77790c88af014d8e95f12134a403cb13",
+ "deepnote_app_block_order": 44,
+ "deepnote_app_block_visible": true,
+ "deepnote_big_number_comparison_enabled": false,
+ "deepnote_big_number_comparison_format": "",
+ "deepnote_big_number_comparison_title": "",
+ "deepnote_big_number_comparison_type": "",
+ "deepnote_big_number_comparison_value": "",
+ "deepnote_big_number_format": "percent",
+ "deepnote_big_number_title": "PV-Anteil Gesamtvb. (%)",
+ "deepnote_big_number_value": "pv_total_consumption_share",
+ "deepnote_cell_type": "big-number",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 1,
+ "execution_start": 1758275932591,
+ "source_hash": "8c0870fe"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"comparisonTitle\": \"\", \"comparisonValue\": \"\", \"title\": \"PV-Anteil Gesamtvb. (%)\", \"value\": \"0\"}'"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "def __deepnote_big_number__():\n",
+ " import json\n",
+ " import jinja2\n",
+ " from jinja2 import meta\n",
+ "\n",
+ " def render_template(template):\n",
+ " parsed_content = jinja2.Environment().parse(template)\n",
+ "\n",
+ " required_variables = meta.find_undeclared_variables(parsed_content)\n",
+ "\n",
+ " context = {\n",
+ " variable_name: globals().get(variable_name)\n",
+ " for variable_name in required_variables\n",
+ " }\n",
+ "\n",
+ " result = jinja2.Environment().from_string(template).render(context)\n",
+ "\n",
+ " return result\n",
+ "\n",
+ " rendered_title = render_template(\"PV-Anteil Gesamtvb. (%)\")\n",
+ " rendered_comparison_title = render_template(\"\")\n",
+ "\n",
+ " return json.dumps({\n",
+ " \"comparisonTitle\": rendered_comparison_title,\n",
+ " \"comparisonValue\": \"\",\n",
+ " \"title\": rendered_title,\n",
+ " \"value\": f\"{pv_total_consumption_share}\"\n",
+ " })\n",
+ "\n",
+ "__deepnote_big_number__()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "cell_id": "262c822a54a74a4b96dfed7ad46b41ac",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 45,
+ "deepnote_app_block_visible": true,
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275932641,
+ "is_code_hidden": true,
+ "source_hash": "bbc108c"
+ },
+ "outputs": [],
+ "source": [
+ "if run_notebook:\n",
+ " _ = display_pv_energy(energy_report_df, resolution)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "19abe1e70d964c74ac88dd5faff57369",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 46,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "text-cell-p",
+ "formattedRanges": [
+ {
+ "fromCodePoint": 0,
+ "marks": {
+ "bold": true
+ },
+ "toCodePoint": 12,
+ "type": "marks"
+ }
+ ]
+ },
+ "source": [
+ "PV-Leistung \n",
+ "Hier sehen Sie sowohl die kumulierte Leistung der PV-Anlagen als auch die gestapelten Einzelleistungen der jeweiligen PV-Anlagen. Die Netzanschluss-Kurve zeigt an, wie viel Stromüberschüsse ins Netz eingespeist wurden (Kurve im negativen Bereich). Beziehungsweise wieviel Strom vom Netz bezogen wurde (Kurve im positiven Bereich).\n",
+ "Um einzelne PV-Anlagen zu filtern muss zunächst \"Alle\" aus dem Filter entfernt und dann die gewünschten PV IDs ausgewählt werden."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "cell_id": "9a0b2460997d4b8bb06b2b7a3bb6b58b",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 47,
+ "deepnote_app_block_visible": true,
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275932692,
+ "is_code_hidden": true,
+ "source_hash": "f26c86bf"
+ },
+ "outputs": [],
+ "source": [
+ "if run_notebook:\n",
+ " pv_columns = [c for c in energy_report_df.columns if \"PV #\" in c]\n",
+ " if 'pv' in component_types:\n",
+ " pv_grid_filter_options = [\"PV und Netzanschluss\", \"Nur PV\", \"Nur Netzanschluss\"]\n",
+ " pv_filter_options = ['Alle'] + [pv[3:] for pv in pv_columns]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "cell_id": "0d452449413546edb8a2c37be14b6fb0",
+ "deepnote_allow_multiple_values": true,
+ "deepnote_app_block_group_id": "e55c5644842748669bb303ed58013f38",
+ "deepnote_app_block_order": 49,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "input-select",
+ "deepnote_input_label": "PV Anlage Filter",
+ "deepnote_variable_custom_options": [
+ "Option 1",
+ "Option 2"
+ ],
+ "deepnote_variable_name": "pv_filter",
+ "deepnote_variable_options": [
+ "Alle",
+ "#259"
+ ],
+ "deepnote_variable_select_type": "from-variable",
+ "deepnote_variable_selected_variable": "pv_filter_options",
+ "deepnote_variable_value": [
+ "Alle"
+ ],
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 1,
+ "execution_start": 1758275932751,
+ "source_hash": "78b17b07"
+ },
+ "outputs": [],
+ "source": [
+ "pv_filter = ['Alle']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "1726c9ed9ef6434e839c2ac01c389093",
+ "deepnote_app_block_group_id": "e55c5644842748669bb303ed58013f38",
+ "deepnote_app_block_order": 50,
+ "deepnote_app_block_visible": true,
+ "deepnote_button_behavior": "set_variable",
+ "deepnote_button_color_scheme": "neutral",
+ "deepnote_button_title": "Filtern",
+ "deepnote_cell_type": "button",
+ "deepnote_variable_name": "Filter_pv",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275932802,
+ "source_hash": "9488b98"
+ },
+ "source": [
+ "Filter_pv = False"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "cell_id": "4a220645a33c47d2a5379f9ca8359490",
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 1,
+ "execution_start": 1758275932851,
+ "is_code_hidden": true,
+ "source_hash": "f9c3319a"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.deepnote.dataframe.v3+json": {
+ "column_count": 0,
+ "columns": [],
+ "preview_row_count": 0,
+ "row_count": 0,
+ "rows": [],
+ "type": "dataframe"
+ },
+ "text/html": [
+ "| \n", + " |
|---|
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "Empty DataFrame\n",
+ "Columns: []\n",
+ "Index: []"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "if run_notebook or Filter_pv:\n",
+ " pv_analyse_df = pd.DataFrame()\n",
+ " pv_filter = [\"All\" if x == \"Alle\" else x for x in pv_filter]\n",
+ "\n",
+ " if 'pv' in component_types and 'timestamp' in energy_report_df.columns:\n",
+ " pv_analyse_df = build_pv_analysis_df(energy_report_df, pv_filter)\n",
+ " pv_analyse_df = mapper.to_display(pv_analyse_df)\n",
+ "\n",
+ "else:\n",
+ " pv_analyse_df = pd.DataFrame()\n",
+ "pv_analyse_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "cell_id": "669a57d9f95c4b0d87d628cfc37f2d5a",
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275932902,
+ "is_code_hidden": true,
+ "source_hash": "6d501105"
+ },
+ "outputs": [],
+ "source": [
+ "if run_notebook:\n",
+ " if \"pv\" in component_types:\n",
+ " fig = plot_time_series(pv_analyse_df, time_col=\"Zeitpunkt\", cols=[\"Netzanschluss\", \"PV Einspeisung\"], title=\"PV Leistung\")\n",
+ " fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "8fa85e393ed9444389be47eac15d1566",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 53,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "text-cell-p",
+ "formattedRanges": [],
+ "is_collapsed": false
+ },
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "b7e1968fa65e42cc80634da9252b564e",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 53,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "text-cell-h2",
+ "formattedRanges": [],
+ "is_collapsed": false
+ },
+ "source": [
+ "## Batterie"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "cell_id": "46cb212074a24598aac8172c2c202821",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 54,
+ "deepnote_app_block_visible": true,
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_app_is_output_hidden": false,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275932952,
+ "is_code_hidden": true,
+ "source_hash": "f4691930"
+ },
+ "outputs": [],
+ "source": [
+ "if run_notebook:\n",
+ " print(f'{datetime.strftime(start_date.astimezone(ZoneInfo(\"CET\")).date(), \"%d.%m.%Y\")} - {datetime.strftime(end_date.astimezone(ZoneInfo(\"CET\")).date(), \"%d.%m.%Y\")}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "7d5428b364a344639ea6f100af6a3f4f",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 55,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "text-cell-p",
+ "formattedRanges": [
+ {
+ "fromCodePoint": 0,
+ "marks": {
+ "bold": true
+ },
+ "toCodePoint": 23,
+ "type": "marks"
+ }
+ ]
+ },
+ "source": [
+ "Batterieleistungskurve \n",
+ "Die Batterieleistungs-Kurve zeigt kumuliert für alle Batterien an, wie viel Strom aus der Batterie bezogen wurde (Kurve im negativen Bereich). Beziehungsweise wieviel Strom in die Batterie eingespeist wurde (Kurve im positiven Bereich). \n",
+ "Um einzelne Batterien zu filtern muss zunächst \"Alle\" aus dem Filter entfernt und dann die gewünschten Batterie IDs ausgewählt werden."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "cell_id": "536da8e56f0e4956aaadf599947f485a",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 56,
+ "deepnote_app_block_visible": true,
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_app_is_output_hidden": false,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275933012,
+ "is_code_hidden": true,
+ "source_hash": "41df19b9"
+ },
+ "outputs": [],
+ "source": [
+ "if run_notebook:\n",
+ " if 'battery' in component_types:\n",
+ " battery_filter_options = ['Alle'] + [f'#{i}' for i in mcfg.component_type_ids('battery')]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "allow_embed": false,
+ "cell_id": "40e60f95f8d242eda8c94858bf357b2e",
+ "deepnote_allow_multiple_values": true,
+ "deepnote_app_block_group_id": "b333a9f757a4456d9c54f6e259453c62",
+ "deepnote_app_block_order": 57,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "input-select",
+ "deepnote_input_label": "Batterie Filter",
+ "deepnote_variable_custom_options": [
+ "Option 1",
+ "Option 2"
+ ],
+ "deepnote_variable_name": "bat_filter",
+ "deepnote_variable_options": [
+ "Alle",
+ "#260",
+ "#263",
+ "#266",
+ "#269",
+ "#272",
+ "#275",
+ "#278",
+ "#281",
+ "#284",
+ "#287",
+ "#290",
+ "#293",
+ "#296"
+ ],
+ "deepnote_variable_select_type": "from-variable",
+ "deepnote_variable_selected_variable": "battery_filter_options",
+ "deepnote_variable_value": [
+ "Alle"
+ ],
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275933072,
+ "source_hash": "916271a"
+ },
+ "outputs": [],
+ "source": [
+ "bat_filter = ['Alle']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "678668530c264079959af52086f0f393",
+ "deepnote_app_block_group_id": "b333a9f757a4456d9c54f6e259453c62",
+ "deepnote_app_block_order": 58,
+ "deepnote_app_block_visible": true,
+ "deepnote_button_behavior": "set_variable",
+ "deepnote_button_color_scheme": "neutral",
+ "deepnote_button_title": "Filtern",
+ "deepnote_cell_type": "button",
+ "deepnote_variable_name": "filter_battery",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275933122,
+ "source_hash": "2c7548d2"
+ },
+ "source": [
+ "filter_battery = False"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "b15644e72758409fa721e1cd2c5b0f71",
+ "deepnote_app_block_group_id": "b333a9f757a4456d9c54f6e259453c62",
+ "deepnote_app_block_order": 59,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "text-cell-p",
+ "formattedRanges": []
+ },
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "cell_id": "57e1fcac9d2c491ca6aabff41e4fc1f7",
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 1,
+ "execution_start": 1758275933172,
+ "is_code_hidden": true,
+ "source_hash": "70d0c912"
+ },
+ "outputs": [],
+ "source": [
+ "if run_notebook or filter_battery:\n",
+ " if \"battery\" in {str(x).lower() for x in component_types}:\n",
+ " bat_filter = [\"All\" if x == \"Alle\" else x for x in bat_filter]\n",
+ " bat_analyse_df = build_component_analysis(energy_report_df, bat_filter, 'Battery', 'Batterie Durchsatz')\n",
+ " bat_analyse_df = mapper.to_display(bat_analyse_df)\n",
+ " fig = plot_time_series(bat_analyse_df, time_col=\"Zeitpunkt\", cols=[\"Batterie Durchsatz\"], title=\"Batterie Durchsatz\", legend_title=None)\n",
+ " fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "fd55e172942748c5be2eb2bf363a806c",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 62,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "text-cell-h2",
+ "formattedRanges": [],
+ "is_collapsed": false
+ },
+ "source": [
+ "## Netzbezug"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "allow_embed": false,
+ "cell_id": "d5d69e9bfbc948159feaa75e7977c91f",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 63,
+ "deepnote_app_block_visible": true,
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_app_is_output_hidden": false,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275933222,
+ "is_code_hidden": true,
+ "source_hash": "f4691930"
+ },
+ "outputs": [],
+ "source": [
+ "if run_notebook:\n",
+ " print(f'{datetime.strftime(start_date.astimezone(ZoneInfo(\"CET\")).date(), \"%d.%m.%Y\")} - {datetime.strftime(end_date.astimezone(ZoneInfo(\"CET\")).date(), \"%d.%m.%Y\")}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "792851ccf6564cc2b733a3f2bfe491cc",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 64,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "text-cell-p",
+ "formattedRanges": [
+ {
+ "fromCodePoint": 0,
+ "marks": {
+ "bold": true
+ },
+ "toCodePoint": 11,
+ "type": "marks"
+ }
+ ]
+ },
+ "source": [
+ "Bezugskurve\n",
+ "Die Bezugskurve zeigt wie viel Strom aus dem Stromnetz bezogen wurde."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "cell_id": "a0160f11409e46d1bd4bc0263e5abb32",
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275933272,
+ "is_code_hidden": true,
+ "source_hash": "dde45d39"
+ },
+ "outputs": [],
+ "source": [
+ "if run_notebook:\n",
+ " fig = plot_time_series(overview_df, time_col=\"Zeitpunkt\", cols=[\"Netzbezug\"], title=\"Netzbezug\")\n",
+ " fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "f35eb0d9cf6544e99221a37afe68cc10",
+ "deepnote_cell_type": "text-cell-h1",
+ "formattedRanges": []
+ },
+ "source": [
+ "# CHP"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "cell_id": "68bae17f70dd4712b2cdfd90655792e7",
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275933322,
+ "is_code_hidden": true,
+ "source_hash": "71a4bce9"
+ },
+ "outputs": [],
+ "source": [
+ "if run_notebook:\n",
+ " if 'chp' in component_types:\n",
+ " chp_filter_options = ['Alle'] + [f'#{i}' for i in mcfg.component_type_ids('chp')]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {
+ "allow_embed": false,
+ "cell_id": "ef4d6cbba2b8428a9264e367787022b6",
+ "deepnote_allow_multiple_values": true,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "input-select",
+ "deepnote_input_label": "CHP Filter",
+ "deepnote_variable_custom_options": [
+ "Option 1",
+ "Option 2"
+ ],
+ "deepnote_variable_default_value": "",
+ "deepnote_variable_name": "chp_filter",
+ "deepnote_variable_options": [
+ "Alle",
+ "#258"
+ ],
+ "deepnote_variable_select_type": "from-variable",
+ "deepnote_variable_selected_variable": "chp_filter_options",
+ "deepnote_variable_value": [
+ "Alle"
+ ],
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275933372,
+ "source_hash": "44b433ed"
+ },
+ "outputs": [],
+ "source": [
+ "chp_filter = ['Alle']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "77673dee82c341debc6765249a29df85",
+ "deepnote_button_behavior": "set_variable",
+ "deepnote_button_color_scheme": "neutral",
+ "deepnote_button_title": "Filtern",
+ "deepnote_cell_type": "button",
+ "deepnote_variable_name": "filter_chp",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 1,
+ "execution_start": 1758275933431,
+ "source_hash": "d5d54beb"
+ },
+ "source": [
+ "filter_chp = False"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {
+ "cell_id": "e0125381471740b19a806dc20dc30d53",
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275933492,
+ "is_code_hidden": true,
+ "source_hash": "19a550d5"
+ },
+ "outputs": [],
+ "source": [
+ "if run_notebook or filter_chp:\n",
+ " if \"chp\" in {str(x).lower() for x in component_types}:\n",
+ " chp_filter = [\"All\" if x == \"Alle\" else x for x in chp_filter]\n",
+ " chp_analyse_df = build_component_analysis(energy_report_df, chp_filter, 'CHP', 'CHP Usage')\n",
+ " chp_analyse_df = mapper.to_display(chp_analyse_df)\n",
+ " fig = plot_time_series(chp_analyse_df, time_col=\"Zeitpunkt\", cols=[\"CHP Usage\"], title=\"CHP\", legend_title=None)\n",
+ " fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "db94528695284b41b98322ae1b776e5e",
+ "deepnote_cell_type": "text-cell-h1",
+ "formattedRanges": []
+ },
+ "source": [
+ "# EV"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {
+ "cell_id": "c9ce293960c8433d88ed6782fa72d0aa",
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275933553,
+ "is_code_hidden": true,
+ "source_hash": "b52555c1"
+ },
+ "outputs": [],
+ "source": [
+ "if run_notebook:\n",
+ " if 'ev' in component_types:\n",
+ " ev_filter_options = ['Alle'] + [f'#{i}' for i in mcfg.component_type_ids('ev')]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {
+ "allow_embed": false,
+ "cell_id": "b8d53a5b1b6c488db55ab567212dc808",
+ "deepnote_allow_multiple_values": true,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "input-select",
+ "deepnote_input_label": "EV Filter",
+ "deepnote_variable_custom_options": [
+ "Option 1",
+ "Option 2"
+ ],
+ "deepnote_variable_default_value": "",
+ "deepnote_variable_name": "ev_filter",
+ "deepnote_variable_options": [
+ "Alle",
+ "#528"
+ ],
+ "deepnote_variable_select_type": "from-variable",
+ "deepnote_variable_selected_variable": "ev_filter_options",
+ "deepnote_variable_value": "Alle",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275933612,
+ "source_hash": "c8b2f31e"
+ },
+ "outputs": [],
+ "source": [
+ "ev_filter = ['Alle']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "allow_embed": false,
+ "cell_id": "69b1250c2de346f6a5db54dbe40e1624",
+ "deepnote_button_behavior": "set_variable",
+ "deepnote_button_color_scheme": "neutral",
+ "deepnote_button_title": "Filtern",
+ "deepnote_cell_type": "button",
+ "deepnote_variable_name": "filter_ev",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275933662,
+ "source_hash": "5621a508"
+ },
+ "source": [
+ "filter_ev = False"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {
+ "cell_id": "8a83df8b97c841bf9f11e4c20aad18e6",
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 1,
+ "execution_start": 1758275933721,
+ "is_code_hidden": true,
+ "source_hash": "c874ed2a"
+ },
+ "outputs": [],
+ "source": [
+ "if run_notebook or filter_ev:\n",
+ " if \"ev\" in {str(x).lower() for x in component_types}:\n",
+ " ev_filter = [\"All\" if x == \"Alle\" else x for x in ev_filter]\n",
+ " ev_analyse_df = build_component_analysis(energy_report_df, ev_filter, 'EV', 'EV Usage')\n",
+ " ev_analyse_df = mapper.to_display(ev_analyse_df)\n",
+ " fig = plot_time_series(ev_analyse_df, time_col=\"Zeitpunkt\", cols=[\"EV Usage\"], title=\"EV\", legend_title=None)\n",
+ " fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "06102019e90441b4bf67f051758d9628",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 66,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "text-cell-h2",
+ "formattedRanges": []
+ },
+ "source": [
+ "## Daten"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {
+ "cell_id": "bd479eb15e014a2ab3b26dc39993239f",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 67,
+ "deepnote_app_block_visible": true,
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_app_is_output_hidden": false,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275933782,
+ "is_code_hidden": true,
+ "source_hash": "f4691930"
+ },
+ "outputs": [],
+ "source": [
+ "if run_notebook:\n",
+ " print(f'{datetime.strftime(start_date.astimezone(ZoneInfo(\"CET\")).date(), \"%d.%m.%Y\")} - {datetime.strftime(end_date.astimezone(ZoneInfo(\"CET\")).date(), \"%d.%m.%Y\")}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "2035d1353f764f5691d7e01b237111ad",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 69,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "text-cell-h3",
+ "formattedRanges": []
+ },
+ "source": [
+ "### Daten exportieren"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "60e17e2af6b2441090cbb5759d33bea6",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 70,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "text-cell-p",
+ "formattedRanges": [
+ {
+ "fromCodePoint": 124,
+ "ranges": [],
+ "toCodePoint": 137,
+ "type": "link",
+ "url": "google.com"
+ }
+ ]
+ },
+ "source": [
+ "Die Daten werden unter angegebenem Dateinamen in den Deepnote Files gespeichert und können von dort heruntergeladen werden.\n",
+ "Mehr erfahren"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {
+ "cell_id": "d9c37e107665477984792ed5a9ca8edf",
+ "deepnote_app_block_group_id": "cd6f5514d1bd4d4c875db036488ed3b7",
+ "deepnote_app_block_order": 71,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "input-text",
+ "deepnote_input_label": "Dateiname (.csv oder .xlsx)",
+ "deepnote_variable_name": "output",
+ "deepnote_variable_value": "",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 1,
+ "execution_start": 1758275933831,
+ "source_hash": "f512bf09"
+ },
+ "outputs": [],
+ "source": [
+ "output = ''"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "55a2edf436f1487ca215c35118aca21e",
+ "deepnote_app_block_group_id": "cd6f5514d1bd4d4c875db036488ed3b7",
+ "deepnote_app_block_order": 72,
+ "deepnote_app_block_visible": true,
+ "deepnote_button_behavior": "run",
+ "deepnote_button_color_scheme": "blue",
+ "deepnote_button_title": "Daten exportieren",
+ "deepnote_cell_type": "button",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275933892,
+ "source_hash": "b623e53d"
+ },
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "97ec1dd98b7e4bd4b07c19d0ec415da0",
+ "deepnote_app_block_group_id": "cd6f5514d1bd4d4c875db036488ed3b7",
+ "deepnote_app_block_order": 73,
+ "deepnote_app_block_visible": true,
+ "deepnote_cell_type": "text-cell-p",
+ "formattedRanges": []
+ },
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {
+ "cell_id": "4008a18973d24e39a8cebf909cf540dd",
+ "deepnote_app_block_group_id": null,
+ "deepnote_app_block_order": 74,
+ "deepnote_app_block_visible": true,
+ "deepnote_app_is_code_hidden": true,
+ "deepnote_cell_type": "code",
+ "execution_context_id": "9ed01f74-bc5f-48b0-bf64-544988d2275c",
+ "execution_millis": 0,
+ "execution_start": 1758275933941,
+ "is_code_hidden": true,
+ "source_hash": "2d6cfdc"
+ },
+ "outputs": [],
+ "source": [
+ "if output:\n",
+ " if os.path.exists(output):\n",
+ " print(\"Fehler: Die Daten wurden nicht exportiert. Es gibt bereits eine Datei mit diesem Namen.\")\n",
+ " elif output.endswith(\".csv\"):\n",
+ " master_df['Zeitpunkt'] = master_df['Zeitpunkt'].dt.strftime(\"%d.%m.%Y %H:%M:%S\")\n",
+ " master_df.to_csv(output, index=False)\n",
+ " print(f\"Die Daten wurden erfolgreich in {output} exportiert.\")\n",
+ " elif output.endswith(\".xlsx\"):\n",
+ " master_df['Zeitpunkt'] = master_df['Zeitpunkt'].dt.strftime(\"%d.%m.%Y %H:%M:%S\")\n",
+ " master_df.to_excel(output, index=False)\n",
+ " print(f\"Die Daten wurden erfolgreich in {output} exportiert.\")\n",
+ " else:\n",
+ " print(\"Fehler: Die Daten wurden nicht exportiert. Output muss .csv oder .xlsx sein.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "cell_id": "b9a60a2bbc2d4e66b2f68f5a3ffce6ed",
+ "deepnote_cell_type": "text-cell-p",
+ "formattedRanges": []
+ },
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "created_in_deepnote_cell": true,
+ "deepnote_cell_type": "markdown"
+ },
+ "source": [
+ "\n",
+ "| \n", + " |
|---|
\n",
+ "Created in Deepnote"
+ ]
+ }
+ ],
+ "metadata": {
+ "deepnote_app_clear_outputs": false,
+ "deepnote_app_embed_enabled": true,
+ "deepnote_app_hide_all_code_blocks_enabled": false,
+ "deepnote_app_layout": "powerful-article",
+ "deepnote_app_reactivity_enabled": true,
+ "deepnote_app_run_on_input_enabled": false,
+ "deepnote_app_run_on_load_enabled": false,
+ "deepnote_app_table_of_contents_enabled": true,
+ "deepnote_notebook_id": "7651f1df2ac94b7b97b7b494e8f08a84",
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pyproject.toml b/pyproject.toml
index 774c793e..f2096604 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -39,6 +39,8 @@ dependencies = [
"kaleido >= 0.2.1, < 1.1.0",
"frequenz-client-reporting >= 0.19.0, < 0.20.0",
"frequenz-client-weather >= 0.2.3, < 0.3.0",
+ "pyyaml>=6.0.2",
+ "types-pyyaml>=6.0.12.20250915",
]
dynamic = ["version"]
diff --git a/src/frequenz/lib/notebooks/reporting/data_processing.py b/src/frequenz/lib/notebooks/reporting/data_processing.py
new file mode 100644
index 00000000..d9da2ffd
--- /dev/null
+++ b/src/frequenz/lib/notebooks/reporting/data_processing.py
@@ -0,0 +1,115 @@
+# License: MIT
+# Copyright © 2025 Frequenz Energy-as-a-Service GmbH
+
+"""Microgrid Reporting DataFrame Construction.
+
+This module constructs normalized energy-report DataFrames from
+raw microgrid telemetry by harmonizing timestamps and column naming,
+enriching PV flows, adding grid KPIs, and surfacing component-specific
+metrics used downstream for dashboards.
+
+Key Features
+------------
+- Energy Report DataFrame Construction
+ - :func:`create_energy_report_df`: Builds a normalized energy report table with
+ unified naming, timezone conversion, grid import calculation, and
+ component renaming based on a MicrogridConfig.
+
+Usage
+-----
+Use create_energy_report_dfs() inside reporting pipelines or notebooks to
+transform raw microgrid exports into localized, labeled, and analysis-ready
+tables for KPIs, dashboards, and stakeholder reporting.
+"""
+
+
+from typing import List
+
+import pandas as pd
+
+from frequenz.data.microgrid.config import MicrogridConfig
+from frequenz.lib.notebooks.reporting.utils.column_mapper import ColumnMapper
+from frequenz.lib.notebooks.reporting.utils.helpers import (
+ _add_pv_energy_flows,
+ _convert_timezone,
+ add_net_grid_import,
+ get_energy_report_columns,
+ label_component_columns,
+)
+
+
+# pylint: disable=too-many-arguments, disable=too-many-locals
+def create_energy_report_dfs(
+ df: pd.DataFrame,
+ component_types: List[str],
+ mcfg: MicrogridConfig,
+ mapper: ColumnMapper,
+ *,
+ tz_name: str = "Europe/Berlin",
+ assume_tz: str = "UTC",
+) -> pd.DataFrame:
+ """Create a normalized Energy Report DataFrame with selected columns.
+
+ Makes a copy of the input, converts the timestamp column to the configured
+ timezone, renames standard columns to unified names, adds the net import
+ column, renames numeric component IDs to labeled names, and returns a
+ reduced DataFrame containing only relevant columns.
+
+ Args:
+ df: Raw input table containing energy data.
+ component_types: Component types to include in the Energy Report DataFrame
+ (e.g., ``battery``, ``pv``).
+ mcfg: Configuration object used to resolve component IDs.
+ mapper: Column Mapper object to standardize the column names.
+ tz_name: Target timezone name for timestamp conversion (default: "Europe/Berlin").
+ assume_tz: Timezone to assume for naive datetimes before conversion (default: "UTC").
+
+ Returns:
+ The Energy Report DataFrame with standardized and selected columns.
+
+ Notes:
+ Component IDs are renamed to labeled names via ``label_component_columns()``.
+ """
+ energy_report_df = df.copy()
+
+ # Only reset index if it's a datetime or period index and 'timestamp' column is missing
+ if isinstance(energy_report_df.index, (pd.DatetimeIndex, pd.PeriodIndex)):
+ if "timestamp" not in energy_report_df.columns:
+ energy_report_df = energy_report_df.reset_index(names="timestamp")
+
+ # Add PV energy flow columns
+ energy_report_df = _add_pv_energy_flows(energy_report_df)
+
+ # Standardize column names (from raw to canonical)
+ energy_report_df = mapper.to_canonical(energy_report_df)
+
+ # Convert timezone
+ energy_report_df = _convert_timezone(
+ energy_report_df,
+ column_timestamp="timestamp",
+ target_tz=tz_name,
+ assume_tz=assume_tz,
+ )
+
+ # Add grid consumption column
+ energy_report_df = add_net_grid_import(
+ energy_report_df,
+ column_grid="grid",
+ column_net_import="net_import",
+ )
+
+ # Helper to rename numeric component IDs to labeled names like PV #250, Battery #219
+ energy_report_df, single_components = label_component_columns(
+ energy_report_df,
+ mcfg,
+ column_battery="battery",
+ column_pv="pv",
+ )
+
+ energy_report_df_cols = get_energy_report_columns(
+ component_types, single_components
+ )
+
+ # Select only the relevant columns
+ energy_report_df = energy_report_df[energy_report_df_cols]
+ return energy_report_df
diff --git a/src/frequenz/lib/notebooks/reporting/utils/__init__.py b/src/frequenz/lib/notebooks/reporting/utils/__init__.py
new file mode 100644
index 00000000..60996680
--- /dev/null
+++ b/src/frequenz/lib/notebooks/reporting/utils/__init__.py
@@ -0,0 +1,4 @@
+# License: MIT
+# Copyright © 2025 Frequenz Energy-as-a-Service GmbH
+
+"""Initialise the utility helper modules."""
diff --git a/src/frequenz/lib/notebooks/reporting/utils/helpers.py b/src/frequenz/lib/notebooks/reporting/utils/helpers.py
new file mode 100644
index 00000000..5a330c28
--- /dev/null
+++ b/src/frequenz/lib/notebooks/reporting/utils/helpers.py
@@ -0,0 +1,329 @@
+# License: MIT
+# Copyright © 2025 Frequenz Energy-as-a-Service GmbH
+"""Helper function for Microgrid Data Processing Utilities.
+
+This module provides utility functions for preprocessing and analyzing microgrid
+data represented in pandas DataFrames. It standardizes column names, handles
+timezone conversions, computes grid imports, derives photovoltaic (PV) energy flows,
+and renames component-specific columns based on a MicrogridConfig.
+
+Key Features
+------------
+- Timezone Conversion
+ Ensures all timestamps are consistently localized
+ (default: UTC → Europe/Berlin).
+
+- Grid Data Processing
+ Extracts net grid import by filtering positive values
+ from grid connection signals.
+
+- PV Energy Flow Calculations
+ Derives PV production, excess, self-consumption, battery charging, and
+ grid feed-in metrics, including PV self-consumption share.
+
+- Component Renaming
+ Maps numeric string component IDs to human-readable labels
+ (e.g., "Battery #14", "PV #7") using the provided MicrogridConfig.
+
+- Reporting Column Assembly
+ Builds the column sets required for downstream energy reports
+ based on the available component types.
+
+Usage
+-----
+These functions serve as building blocks for energy reporting, data pipelines,
+and dashboards that analyze microgrid performance, particularly in hybrid systems
+with PV, batteries, and grid interactions.
+"""
+
+from typing import Any, Dict, List, Tuple
+
+import pandas as pd
+import yaml
+
+from frequenz.data.microgrid.config import MicrogridConfig
+
+
+def load_config(path: str) -> Dict[str, Any]:
+ """
+ Load a YAML config file and return it as a dictionary.
+
+ Args:
+ path: Path to the YAML file.
+
+ Returns:
+ Configuration values as a dictionary.
+
+ Raises:
+ TypeError: If the YAML root element is not a mapping (dict).
+ """
+ with open(path, "r", encoding="utf-8") as f:
+ data = yaml.safe_load(f)
+
+ if not isinstance(data, dict):
+ raise TypeError("YAML root must be a mapping (dict).")
+
+ return data
+
+
+def _fmt_de(x: float) -> str:
+ """Format a number using German-style decimal and thousands separators.
+
+ The function formats the number with two decimal places, using a comma
+ as the decimal separator and a dot as the thousands separator.
+
+ Args:
+ x: The number to format.
+
+ Returns:
+ The formatted string with German number formatting applied.
+
+ Example:
+ >>> _fmt_de(12345.6789)
+ '12.345,68'
+ """
+ return f"{x:,.2f}".replace(",", "X").replace(".", ",").replace("X", ".")
+
+
+def _convert_timezone(
+ df: pd.DataFrame,
+ column_timestamp: str,
+ target_tz: str = "Europe/Berlin",
+ assume_tz: str = "UTC",
+) -> pd.DataFrame:
+ """Convert a datetime column in a DataFrame to a target timezone.
+
+ If the column contains timezone-naive datetimes, they are first localized to
+ ``assume_tz`` before being converted to ``target_tz``.
+
+ Args:
+ df: Input DataFrame containing the datetime column.
+ column_timestamp: Name of the datetime column in ``df`` to convert.
+ target_tz: Timezone name to convert the column to.
+ Defaults to ``"Europe/Berlin"``.
+ assume_tz: Timezone to assume for naive datetimes.
+ Defaults to ``"UTC"``.
+
+ Returns:
+ pd.DataFrame: A copy of the DataFrame with the converted datetime column.
+
+ Raises:
+ ValueError: If ``column_timestamp`` is not present in ``df``.
+ """
+ if column_timestamp not in df:
+ raise ValueError(f"{column_timestamp} column not in df")
+
+ ts = df[column_timestamp]
+
+ if ts.dt.tz is None:
+ # Assume naïve datetimes are in `assume_tz`
+ ts = ts.dt.tz_localize(assume_tz)
+
+ df[column_timestamp] = ts.dt.tz_convert(target_tz)
+ return df
+
+
+def add_net_grid_import(
+ df: pd.DataFrame,
+ column_grid: str,
+ column_net_import: str,
+) -> pd.DataFrame:
+ """Calculate grid consumption and add it as ``column_net_import``.
+
+ Grid consumption is defined as the positive part of ``column_grid``.
+ Negative values are replaced with 0.
+
+ Args:
+ df: Input DataFrame containing the grid data.
+ column_grid: Name of the column in ``df`` that contains grid values.
+ column_net_import: Name of the output column to store the computed
+ net import values.
+
+ Returns:
+ pd.DataFrame: The DataFrame with a new or updated ``column_net_import`` column.
+
+ Raises:
+ ValueError: If ``column_grid`` is not present in ``df``.
+ """
+ if column_grid not in df:
+ raise ValueError(f"{column_grid} column not in df")
+
+ df[column_net_import] = df[column_grid].apply(lambda x: x if x > 0 else 0)
+ return df
+
+
+# pylint: disable=too-many-arguments, too-many-positional-arguments
+def label_component_columns(
+ df: pd.DataFrame,
+ mcfg: MicrogridConfig,
+ column_battery: str = "battery",
+ column_pv: str = "pv",
+ column_chp: str = "chp",
+ column_ev: str = "ev",
+) -> Tuple[pd.DataFrame, List[str]]:
+ """Rename numeric single-component columns to labeled names.
+
+ Numeric string column names like ``"14"`` are converted to
+ ``"Battery #14"``, ``"PV #14"``, ``"CHP #14"`` or ``"EV #14"`` based on
+ the component IDs provided by ``mcfg.component_type_ids(...)``
+
+ Args:
+ df: Input DataFrame with numeric string column names.
+ mcfg: Configuration with ``_component_types_cfg`` mapping component types to a
+ ``meter`` iterable of numeric IDs.
+ column_battery: Key name for battery component type.
+ column_pv: Key name for PV component type.
+ column_chp: Key name for CHP component type.
+ column_ev: Key name for EV component type
+ Returns:
+ Tuple containing the renamed DataFrame and the list of applied labels
+ """
+ # Numeric component columns present in df
+ single_components = [str(c) for c in df.columns if str(c).isdigit()]
+ available_types = set(mcfg.component_types())
+
+ # From config (empty set if missing)
+ def ids_if_available(t: str) -> set[str]:
+ return (
+ {str(x) for x in mcfg.component_type_ids(t)}
+ if t in available_types
+ else set()
+ )
+
+ battery_ids = ids_if_available(column_battery)
+ pv_ids = ids_if_available(column_pv)
+ chp_ids = ids_if_available(column_chp)
+ ev_ids = ids_if_available(column_ev)
+
+ rename: Dict[str, str] = {}
+ rename.update(
+ {
+ c: f"{column_battery.capitalize()} #{c}"
+ for c in single_components
+ if c in battery_ids
+ }
+ )
+ rename.update(
+ {c: f"{column_pv.upper()} #{c}" for c in single_components if c in pv_ids}
+ )
+ rename.update(
+ {c: f"{column_ev.upper()} #{c}" for c in single_components if c in ev_ids}
+ )
+ rename.update(
+ {c: f"{column_chp.upper()} #{c}" for c in single_components if c in chp_ids}
+ )
+
+ return df.rename(columns=rename), list(rename.values())
+
+
+def _add_pv_energy_flows(df: pd.DataFrame) -> pd.DataFrame:
+ """Add PV-related energy flow columns to ``df`` if PV data is present.
+
+ Derives photovoltaic (PV) energy-flow metrics from existing columns. If no PV
+ signal is present (i.e., the negative PV column is missing or all zeros), the
+ DataFrame is returned unchanged.
+
+ Args:
+ df: Input DataFrame. If present, uses columns ``pv_neg``,
+ ``consumption``, and ``COLUMN_BATTERY_POS``. Missing columns are
+ treated as zeros.
+
+ Returns:
+ The DataFrame with added PV flow columns (or unchanged if no PV signal).
+
+ Notes:
+ Newly created/updated columns:
+ - ``COLUMN_PV_PROD``: PV production as a positive series (negated/clipped from
+ ``pv_neg``).
+ - ``COLUMN_PV_EXCESS``: Excess PV after subtracting household consumption.
+ - ``COLUMN_PV_BAT``: Portion of PV excess routed into the battery (bounded by
+ battery charge).
+ - ``COLUMN_PV_FEEDIN``: PV fed into the grid after battery charging.
+ - ``COLUMN_PV_SELF``: Self-consumed PV (production minus excess).
+ - ``COLUMN_PV_SHARE``: Share of consumption covered by self-consumed PV (NaN
+ when consumption is 0).
+ """
+ # Safe inputs (0 if missing)
+ df_with_pv_flows = df.copy()
+ zeros = pd.Series(0, index=df_with_pv_flows.index)
+ pv_neg = df_with_pv_flows.get("pv_neg", zeros)
+ consumption = df_with_pv_flows.get("consumption", zeros)
+ battery_pos = df_with_pv_flows.get("battery_pos", zeros)
+
+ # Only compute PV features if there is any PV signal
+ has_pv = isinstance(pv_neg, pd.Series) and (pv_neg != 0).any()
+ if not has_pv:
+ return df_with_pv_flows
+
+ df_with_pv_flows["pv_prod"] = (-pv_neg).clip(lower=0)
+ df_with_pv_flows["pv_excess"] = (df_with_pv_flows["pv_prod"] - consumption).clip(
+ lower=0
+ )
+
+ # This naturally becomes 0 when there's no battery_pos column
+ df_with_pv_flows["pv_bat"] = pd.concat(
+ [df_with_pv_flows["pv_excess"], battery_pos], axis=1
+ ).min(axis=1)
+
+ df_with_pv_flows["pv_feedin"] = (
+ df_with_pv_flows["pv_excess"] - df_with_pv_flows["pv_bat"]
+ )
+ df_with_pv_flows["pv_self"] = (
+ df_with_pv_flows["pv_prod"] - df_with_pv_flows["pv_excess"]
+ ).clip(lower=0)
+
+ denom = consumption.replace(0, pd.NA)
+ df_with_pv_flows["pv_share"] = df_with_pv_flows["pv_self"] / denom
+
+ return df_with_pv_flows
+
+
+def get_energy_report_columns(
+ component_types: List[str], single_components: List[str]
+) -> List[str]:
+ """Build the list of dataframe columns for the energy report.
+
+ The selected columns depend on the available component types.
+
+ Args:
+ component_types: List of component types (e.g. ["pv", "battery"])
+ single_components: Extra component columns to always include.
+
+ Returns:
+ The full list of dataframe columns.
+ """
+ # Base columns
+ energy_report_df_cols = [
+ "timestamp",
+ "grid",
+ "net_import",
+ "net_consumption",
+ ] + single_components
+
+ # Map component types to the columns they enable
+ component_column_map = {
+ "battery": ["battery_throughput"],
+ "pv": [
+ "pv_throughput",
+ "pv_prod",
+ "pv_self",
+ "pv_feedin",
+ ],
+ }
+
+ # Define columns that require both PV and Battery
+ pv_battery_cols = [
+ "pv_in_bat",
+ "pv_share",
+ ]
+
+ # Add component-specific columns
+ for component, columns in component_column_map.items():
+ if component in component_types:
+ energy_report_df_cols.extend(columns)
+
+ # Add combined PV + Battery columns
+ if "pv" in component_types and "battery" in component_types:
+ energy_report_df_cols.extend(pv_battery_cols)
+
+ return energy_report_df_cols
diff --git a/src/frequenz/lib/notebooks/reporting/utils/reporting_nb_functions.py b/src/frequenz/lib/notebooks/reporting/utils/reporting_nb_functions.py
new file mode 100644
index 00000000..cab07824
--- /dev/null
+++ b/src/frequenz/lib/notebooks/reporting/utils/reporting_nb_functions.py
@@ -0,0 +1,490 @@
+# License: MIT
+# Copyright © 2025 Frequenz Energy-as-a-Service GmbH
+
+"""Microgrid Reporting and Analysis Utilities for the notebook.
+
+This module provides helpers to compute energy summaries, normalize per-component
+timeseries to tidy (long) tables, and derive high-level KPIs.
+
+Key features
+-------------
+1) compute_energy_summary(df, resolution) -> DataFrame
+ Aggregates PV and grid energy over the time step and returns a compact table with:
+ - "Energy Source" (PV / Grid)
+ - "Energy [kWh]" (sum over period)
+ - "Energy %" (share of total)
+ - "Power [kW]" (avg power = energy / hours)
+ Looks for "net_import" (grid) and "pv_self" (self-consumed PV). Missing series
+ are treated as zero; rows are ordered PV first (if present), then Grid.
+
+2) display_pv_energy(energy_report_df, resolution, label_contains="PV #", …) -> dict
+ Prints per-component PV energies in kWh (supports German formatting) and returns
+ a dict of {component_label: kWh}. Optionally prints and returns "PV Total".
+ Uses the convention that PV power is negative and multiplies by -1.
+
+3) build_pv_analysis_df(energy_report_df, pv_filter) -> DataFrame
+ Creates a tidy, long-form PV table by melting selected "PV #N" columns into:
+ - "timestamp"
+ - optional "grid" (evenly divided across selected PVs if present)
+ - "PV" (component label without the "PV " prefix, e.g., "#1")
+ - "pv_feedin" (positive production after sign flip)
+ `pv_filter` accepts explicit IDs like ["#1", "#3"] or ["all"] (case-insensitive).
+
+4) build_component_analysis(energy_report_df, selection_filter,
+ component_label, value_col_name) -> DataFrame
+ Generic version of (3) for any component family, e.g. ("Battery", "battery").
+ Returns long form with columns: "timestamp",