minor changes

component_test_connectors/heat_pump_connection_read_data.py

@@ -0,0 +1,7 @@
+from heat_pump import HeatPump
+
+hp_master = HeatPump(device_name='hp_master', ip_address='10.0.0.10', port=502, excel_path="../modbus_registers/heat_pump_registers.xlsx")
+
+state = hp_master.get_state()
+
+print(state)

component_test_connectors/heat_pump_connection_sg_ready.py

@@ -0,0 +1,49 @@
+from pymodbus.client import ModbusTcpClient
+
+def switch_sg_ready_mode(ip, port, mode):
+    """
+    Register 300: 1=BUS 0= Hardware Kontakte
+    Register 301 & 302:
+        0-0= Kein Offset
+        0-1 Boiler und Heizung Offset
+        1-1 Boiler Offset + E-Einsatz Sollwert Erhöht
+        1-0 SG EVU Sperre
+    :param ip:
+    :param mode:
+    'mode1' = [True, False, False] => SG Ready deactivated
+    'mode2' = [True, False, True] => SG ready activated for heatpump only
+    'mode3' = [True, True, True] => SG ready activated for heatpump and heat rod
+    :return:
+    """
+    client = ModbusTcpClient(ip, port=port)
+    if not client.connect():
+        print("Verbindung zur Wärmepumpe fehlgeschlagen.")
+        return
+
+    mode_code = None
+    if mode == 'mode1':
+        mode_code = [True, False, False]
+    elif mode == 'mode2':
+        mode_code = [True, False, True]
+    elif mode == 'mode3':
+        mode_code = [True, True, True]
+    else:
+        print('Uncorrect or no string for mode!')
+
+    try:
+        response_300 = client.write_coil(300, mode_code[0])
+        response_301 = client.write_coil(301, mode_code[1])
+        response_302 = client.write_coil(302, mode_code[2])
+
+        # Optional: Rückmeldungen prüfen
+        for addr, resp in zip([300, 301, 302], [response_300, response_301, response_302]):
+            if resp.isError():
+                print(f"Fehler beim Schreiben von Coil {addr}: {resp}")
+            else:
+                print(f"Coil {addr} erfolgreich geschrieben.")
+
+    finally:
+        client.close()
+
+if '__name__' == '__main__':
+    switch_sg_ready_mode(ip='10.0.0.10', port=502, mode='mode2')

data_base_influx.py

@@ -1,5 +1,7 @@
 from influxdb_client import InfluxDBClient, Point, WritePrecision
 from datetime import datetime
+import datetime as dt
+import pandas as pd
 
 class DataBaseInflux:
     def __init__(self, url: str, token: str, org: str, bucket: str):
@@ -25,4 +27,22 @@ class DataBaseInflux:
         # Punkt in InfluxDB schreiben
         self.write_api.write(bucket=self.bucket, org=self.org, record=point)
 
+    def store_forecasts(self, forecast_name: str, data: pd.Series):
+
+        measurement = forecast_name
+        run_tag = dt.datetime.now(dt.timezone.utc).replace(second=0, microsecond=0).isoformat(timespec="minutes")
+
+        pts = []
+
+        series = pd.to_numeric(data, errors="coerce").dropna()
+
+        for ts, val in series.items():
+            pts.append(
+                Point(measurement)
+                .tag("run", run_tag)
+                .field("value", float(val))
+                .time(ts.to_pydatetime(), WritePrecision.S)
+            )
+
+        self.write_api.write(bucket=self.bucket, org=self.org, record=pts)
 

data_base_operations/transform_old_db_to_new.py

@@ -0,0 +1,213 @@
+import os, re, math, time
+from datetime import datetime, timezone, timedelta
+
+import pandas as pd
+from influxdb_client import InfluxDBClient, Point, WritePrecision
+from influxdb_client.client.write_api import SYNCHRONOUS
+from influxdb_client.rest import ApiException
+
+
+# -----------------------
+# CONFIG
+# -----------------------
+INFLUX_URL = "http://192.168.1.146:8086"
+INFLUX_ORG = "allmende"
+INFLUX_TOKEN = os.environ.get("INFLUX_TOKEN", "Cw_naEZyvJ3isiAh1P4Eq3TsjcHmzzDFS7SlbKDsS6ZWL04fMEYixWqtNxGThDdG27S9aW5g7FP9eiq5z1rsGA==")
+
+SOURCE_BUCKET = "allmende_db"
+TARGET_BUCKET = "allmende_db_v2"
+
+MEASUREMENTS = [
+    "hp_master", "hp_slave", "pv_forecast", "sg_ready",
+    "solaredge_master", "solaredge_meter", "solaredge_slave", "wohnung_2_6"
+]
+
+START_DT = datetime(2025, 6, 1, tzinfo=timezone.utc)
+STOP_DT = datetime.now(timezone.utc)
+WINDOW = timedelta(days=1)
+
+EXCEL_PATH = "../modbus_registers/heat_pump_registers.xlsx"
+EXCEL_SHEET = "Register_Map"
+
+BATCH_SIZE = 1000
+MAX_RETRIES = 8
+
+
+# -----------------------
+# Helpers
+# -----------------------
+def normalize(s) -> str:
+    s = "" if s is None else str(s).strip()
+    return re.sub(r"\s+", " ", s)
+
+def is_invalid_sentinel(v: float) -> bool:
+    return v in (-999.9, -999.0, 30000.0, 32767.0, 65535.0)
+
+def ensure_bucket(client: InfluxDBClient, name: str):
+    bapi = client.buckets_api()
+    if bapi.find_bucket_by_name(name):
+        return
+    bapi.create_bucket(bucket_name=name, org=INFLUX_ORG, retention_rules=None)
+
+def build_field_type_map_from_excel(path: str) -> dict[str, str]:
+    df = pd.read_excel(path, sheet_name=EXCEL_SHEET)
+    df = df[df["Register_Type"].astype(str).str.upper() == "IR"].copy()
+    df["Address"] = df["Address"].astype(int)
+    df["Description"] = df["Description"].fillna("").astype(str)
+    df["Tag_Name"] = df["Tag_Name"].fillna("").astype(str)
+    df["Data_Type"] = df["Data_Type"].fillna("").astype(str)
+
+    m: dict[str, str] = {}
+    for _, r in df.iterrows():
+        addr = int(r["Address"])
+        desc = normalize(r["Description"])
+        tag  = normalize(r["Tag_Name"])
+        dtp  = normalize(r["Data_Type"]).upper()
+        if tag:
+            m[tag] = dtp
+        old_key = normalize(f"{addr} - {desc}".strip(" -"))
+        if old_key:
+            m[old_key] = dtp
+    return m
+
+def coerce_value_to_dtype(v, dtype: str):
+    if v is None:
+        return None
+    dtp = (dtype or "").upper()
+
+    if isinstance(v, (int, float)):
+        fv = float(v)
+        if math.isnan(fv) or math.isinf(fv):
+            return None
+
+    if dtp in ("BOOL", "BOOLEAN"):
+        if isinstance(v, bool): return v
+        if isinstance(v, (int, float)): return bool(int(v))
+        return None
+
+    if dtp.startswith("INT") or dtp.startswith("UINT"):
+        if isinstance(v, bool): return int(v)
+        if isinstance(v, (int, float)): return int(float(v))
+        return None
+
+    if dtp.startswith("FLOAT") or dtp in ("DOUBLE",):
+        if isinstance(v, bool): return float(int(v))
+        if isinstance(v, (int, float)): return float(v)
+        return None
+
+    return None
+
+def write_with_retry(write_api, batch):
+    delay = 1.0
+    last_msg = ""
+    for _ in range(MAX_RETRIES):
+        try:
+            write_api.write(bucket=TARGET_BUCKET, org=INFLUX_ORG, record=batch)
+            return
+        except ApiException as e:
+            last_msg = getattr(e, "body", "") or str(e)
+            status = getattr(e, "status", None)
+            if "timeout" in last_msg.lower() or status in (429, 500, 502, 503, 504):
+                time.sleep(delay)
+                delay = min(delay * 2, 30)
+                continue
+            raise
+    raise RuntimeError(f"Write failed after {MAX_RETRIES} retries: {last_msg}")
+
+def window_already_migrated(query_api, measurement: str, start: datetime, stop: datetime) -> bool:
+    # Prüft: gibt es im Zielbucket im Fenster mindestens 1 Punkt?
+    flux = f'''
+from(bucket: "{TARGET_BUCKET}")
+  |> range(start: time(v: "{start.isoformat()}"), stop: time(v: "{stop.isoformat()}"))
+  |> filter(fn: (r) => r._measurement == "{measurement}")
+  |> limit(n: 1)
+'''
+    tables = query_api.query(flux, org=INFLUX_ORG)
+    for t in tables:
+        if t.records:
+            return True
+    return False
+
+def migrate_window(query_api, write_api, measurement: str,
+                   start: datetime, stop: datetime,
+                   type_map: dict[str, str],
+                   do_type_cast: bool) -> int:
+    flux = f'''
+from(bucket: "{SOURCE_BUCKET}")
+  |> range(start: time(v: "{start.isoformat()}"), stop: time(v: "{stop.isoformat()}"))
+  |> filter(fn: (r) => r._measurement == "{measurement}")
+  |> keep(columns: ["_time","_measurement","_field","_value"])
+'''
+    tables = query_api.query(flux, org=INFLUX_ORG)
+
+    batch, written = [], 0
+    for table in tables:
+        for rec in table.records:
+            t = rec.get_time()
+            field = normalize(rec.get_field())
+            value = rec.get_value()
+            if value is None:
+                continue
+
+            if do_type_cast:
+                dtp = type_map.get(field)
+                if dtp:
+                    cv = coerce_value_to_dtype(value, dtp)
+                    if cv is None:
+                        continue
+                    if isinstance(cv, (int, float)) and is_invalid_sentinel(float(cv)):
+                        continue
+                    value = cv
+                # kein Mapping -> unverändert schreiben
+
+            batch.append(Point(measurement).field(field, value).time(t, WritePrecision.NS))
+
+            if len(batch) >= BATCH_SIZE:
+                write_with_retry(write_api, batch)
+                written += len(batch)
+                batch = []
+
+    if batch:
+        write_with_retry(write_api, batch)
+        written += len(batch)
+
+    return written
+
+
+# -----------------------
+# Main
+# -----------------------
+def main():
+    if not INFLUX_TOKEN:
+        raise RuntimeError("INFLUX_TOKEN fehlt (Env-Var INFLUX_TOKEN setzen).")
+
+    with InfluxDBClient(url=INFLUX_URL, token=INFLUX_TOKEN, org=INFLUX_ORG, timeout=900_000) as client:
+        ensure_bucket(client, TARGET_BUCKET)
+
+        type_map = build_field_type_map_from_excel(EXCEL_PATH)
+        query_api = client.query_api()
+        write_api = client.write_api(write_options=SYNCHRONOUS)
+
+        for meas in MEASUREMENTS:
+            do_cast = meas in ("hp_master", "hp_slave")
+            cur, total = START_DT, 0
+            print(f"\n== {meas}  (cast={'ON' if do_cast else 'OFF'}) ==")
+
+            while cur < STOP_DT:
+                nxt = min(cur + WINDOW, STOP_DT)
+
+                if window_already_migrated(query_api, meas, cur, nxt):
+                    print(f"{cur.isoformat()} -> {nxt.isoformat()} : SKIP (existiert schon)")
+                    cur = nxt
+                    continue
+
+                n = migrate_window(query_api, write_api, meas, cur, nxt, type_map, do_cast)
+                total += n
+                print(f"{cur.isoformat()} -> {nxt.isoformat()} : {n} (gesamt {total})")
+                cur = nxt
+
+            print(f"== Fertig {meas}: {total} Punkte ==")
+
+
+if __name__ == "__main__":
+    main()

forecaster/weather_forecaster.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python3
+import time
+import datetime as dt
+import requests
+from zoneinfo import ZoneInfo
+from matplotlib import pyplot as plt
+import pandas as pd
+
+TZ = "Europe/Berlin"
+DAYS = 2
+
+OPEN_METEO_URL = "https://api.open-meteo.com/v1/forecast"
+
+class WeatherForecaster:
+    def __init__(self, latitude, longitude):
+        self.lat = latitude
+        self.lon = longitude
+
+    def get_hourly_forecast(self, start_hour, days):
+        start_hour_local = start_hour
+        end_hour_local   = start_hour_local + dt.timedelta(days=days)
+
+        params = {
+            "latitude": self.lat,
+            "longitude": self.lon,
+            "hourly": ["temperature_2m", "shortwave_radiation", "wind_speed_10m"],
+            "timezone": TZ,
+            "start_hour": start_hour_local.strftime("%Y-%m-%dT%H:%M"),
+            "end_hour": end_hour_local.strftime("%Y-%m-%dT%H:%M")
+        }
+
+        h = requests.get(OPEN_METEO_URL, params=params).json()["hourly"]
+
+        time_stamps = h["time"]
+        time_stamps = [
+            dt.datetime.fromisoformat(t).replace(tzinfo=ZoneInfo(TZ))
+            for t in time_stamps
+        ]
+
+        weather = pd.DataFrame(index=time_stamps)
+        weather["ghi"] = h["shortwave_radiation"]
+        weather["temp_air"] = h["temperature_2m"]
+        weather["wind_speed"] = h["wind_speed_10m"]
+
+        return weather
+
+
+
+if __name__=='__main__':
+
+    weather_forecast = WeatherForecaster(latitude=48.041, longitude=7.862)
+    while True:
+        now = dt.datetime.now()
+        secs = 60 - now.second #(60 - now.minute) * 60 - now.second  # Sekunden bis volle Stunde
+        time.sleep(secs)
+
+        now_local = dt.datetime.now()
+        start_hour_local = (now_local + dt.timedelta(hours=1)).replace(minute=0, second=0, microsecond=0)
+        time_stamps, temps, ghi, wind_speed = weather_forecast.get_hourly_forecast(start_hour_local, DAYS)
+        plt.plot(time_stamps, temps)
+        plt.show()

heat_pump.py

@@ -1,64 +1,173 @@
 from pymodbus.client import ModbusTcpClient
 import pandas as pd
 import time
+import struct
+import math
+
 
 class HeatPump:
-    def __init__(self, device_name: str, ip_address: str, port: int=502):
+    def __init__(self, device_name: str, ip_address: str, port: int = 502,
+                 excel_path: str = "modbus_registers/heat_pump_registers.xlsx",
+                 sheet_name: str = "Register_Map"):
         self.device_name = device_name
         self.ip = ip_address
         self.port = port
-        self.client = None
-        self.connect_to_modbus()
-        self.registers = None
-        self.get_registers()
+        self.client = ModbusTcpClient(self.ip, port=self.port)
 
-    def connect_to_modbus(self):
-        port = self.port
-        self.client = ModbusTcpClient(self.ip, port=port)
+        self.excel_path = excel_path
+        self.sheet_name = sheet_name
+        self.registers = self.get_registers()
+
+    # -------------
+    # Connection
+    # -------------
+    def connect(self) -> bool:
+        ok = self.client.connect()
+        if not ok:
+            print("Verbindung zur Wärmepumpe fehlgeschlagen.")
+        return ok
+
+    def close(self):
         try:
-            if not self.client.connect():
-                print("Verbindung zur Wärmepumpe fehlgeschlagen.")
-                exit(1)
-            print("Verbindung zur Wärmepumpe erfolgreich.")
-        except KeyboardInterrupt:
-            print("Beendet durch Benutzer (Ctrl+C).")
-        finally:
             self.client.close()
+        except Exception:
+            pass
 
-    def get_registers(self):
-        # Excel-Datei mit den Input-Registerinformationen
-        excel_path = "modbus_registers/heat_pump_registers.xlsx"
-        xls = pd.ExcelFile(excel_path)
-        df_input_registers = xls.parse('04 Input Register')
+    # -------------
+    # Excel parsing
+    # -------------
+    def get_registers(self) -> dict:
+        df = pd.read_excel(self.excel_path, sheet_name=self.sheet_name)
+        df = df[df["Register_Type"].astype(str).str.upper() == "IR"].copy()
 
-        # Relevante Spalten bereinigen
-        df_clean = df_input_registers[['MB Adresse', 'Variable', 'Beschreibung', 'Variabel Typ']].dropna()
-        df_clean['MB Adresse'] = df_clean['MB Adresse'].astype(int)
+        df["Address"] = df["Address"].astype(int)
+        df["Length"] = df["Length"].astype(int)
+        df["Data_Type"] = df["Data_Type"].astype(str).str.upper()
+        df["Byteorder"] = df["Byteorder"].astype(str).str.upper()
 
-        # Dictionary aus Excel erzeugen
-        self.registers = {
-            row['MB Adresse']: {
-                'desc': row['Beschreibung'],
-                'type': 'REAL' if row['Variabel Typ'] == 'REAL' else 'INT'
+        df["Scaling"] = df.get("Scaling", 1.0)
+        df["Scaling"] = df["Scaling"].fillna(1.0).astype(float)
+
+        df["Offset"] = df.get("Offset", 0.0)
+        df["Offset"] = df["Offset"].fillna(0.0).astype(float)
+
+        regs = {}
+        for _, row in df.iterrows():
+            regs[int(row["Address"])] = {
+                "length": int(row["Length"]),
+                "data_type": row["Data_Type"],
+                "byteorder": row["Byteorder"],
+                "scaling": float(row["Scaling"]),
+                "offset": float(row["Offset"]),
+                "tag": str(row.get("Tag_Name", "")).strip(),
+                "desc": "" if pd.isna(row.get("Description")) else str(row.get("Description")).strip(),
             }
-            for _, row in df_clean.iterrows()
-        }
+        return regs
 
-    def get_state(self):
-        data = {}
-        data['Zeit'] = time.strftime('%Y-%m-%d %H:%M:%S')
-        for address, info in self.registers.items():
-            reg_type = info['type']
-            result = self.client.read_input_registers(address, count=2 if reg_type == 'REAL' else 1)
-            if result.isError():
-                print(f"Fehler beim Lesen von Adresse {address}: {result}")
-                continue
+    # -------------
+    # Byteorder handling
+    # -------------
+    @staticmethod
+    def _registers_to_bytes(registers: list[int], byteorder_code: str) -> bytes:
+        """
+        registers: Liste von uint16 (0..65535), wie pymodbus sie liefert.
+        byteorder_code: AB, ABCD, CDAB, BADC, DCBA (gemäß Template)
+        Rückgabe: bytes in der Reihenfolge, wie sie für struct.unpack benötigt werden.
+        """
+        code = (byteorder_code or "ABCD").upper()
 
-            if reg_type == 'REAL':
-                value = result.registers[0] / 10.0
-            else:
-                value = result.registers[0]
+        # Pro Register: 16-bit => zwei Bytes (MSB, LSB)
+        words = [struct.pack(">H", r & 0xFFFF) for r in registers]  # big endian pro Wort
+
+        if len(words) == 1:
+            w = words[0]  # b'\xAA\xBB'
+            if code in ("AB", "ABCD", "CDAB"):
+                return w
+            if code == "BADC":  # byte swap
+                return w[::-1]
+            if code == "DCBA":  # byte swap (bei 16-bit identisch zu BADC)
+                return w[::-1]
+            return w
+
+        # 32-bit (2 words) oder 64-bit (4 words): Word/Byte swaps abbilden
+        # words[0] = high word bytes, words[1] = low word bytes (in Modbus-Reihenfolge gelesen)
+        if code == "ABCD":
+            ordered = words
+        elif code == "CDAB":
+            # word swap
+            ordered = words[1:] + words[:1]
+        elif code == "BADC":
+            # byte swap innerhalb jedes Words
+            ordered = [w[::-1] for w in words]
+        elif code == "DCBA":
+            # word + byte swap
+            ordered = [w[::-1] for w in (words[1:] + words[:1])]
+        else:
+            ordered = words
+
+        return b"".join(ordered)
+
+    @staticmethod
+    def _decode_by_type(raw_bytes: bytes, data_type: str):
+        dt = (data_type or "").upper()
+
+        # struct: > = big endian, < = little endian
+        # Wir liefern raw_bytes bereits in der richtigen Reihenfolge; daher nutzen wir ">" konsistent.
+        if dt == "UINT16":
+            return struct.unpack(">H", raw_bytes[:2])[0]
+        if dt == "INT16":
+            return struct.unpack(">h", raw_bytes[:2])[0]
+        if dt == "UINT32":
+            return struct.unpack(">I", raw_bytes[:4])[0]
+        if dt == "INT32":
+            return struct.unpack(">i", raw_bytes[:4])[0]
+        if dt == "FLOAT32":
+            return struct.unpack(">f", raw_bytes[:4])[0]
+        if dt == "FLOAT64":
+            return struct.unpack(">d", raw_bytes[:8])[0]
+
+        raise ValueError(f"Unbekannter Data_Type: {dt}")
+
+    def _decode_value(self, registers: list[int], meta: dict):
+        raw = self._registers_to_bytes(registers, meta["byteorder"])
+        val = self._decode_by_type(raw, meta["data_type"])
+        return (val * meta["scaling"]) + meta["offset"]
+
+    # -------------
+    # Reading
+    # -------------
+    def get_state(self) -> dict:
+        data = {"Zeit": time.strftime("%Y-%m-%d %H:%M:%S")}
+
+        if not self.connect():
+            data["error"] = "connect_failed"
+            return data
+
+        try:
+            for address, meta in self.registers.items():
+                count = int(meta["length"])
+                result = self.client.read_input_registers(address, count=count)
+                if result.isError():
+                    print(f"Fehler beim Lesen von Adresse {address}: {result}")
+                    continue
+
+                try:
+                    value = self._decode_value(result.registers, meta)
+                except Exception as e:
+                    print(f"Decode-Fehler an Adresse {address} ({meta.get('tag','')}): {e}")
+                    continue
+
+                # Optional filter
+                # if self._is_invalid_sentinel(value):
+                #     continue
+                value = float(value)
+                desc = meta.get("desc") or ""
+                field_name = f"{address} - {desc}".strip(" -")
+                data[field_name] = float(value)
+
+                print(f"Adresse {address} - {desc}: {value}")
+
+        finally:
+            self.close()
 
-            print(f"Adresse {address} - {info['desc']}: {value}")
-            data[f"{address} - {info['desc']}"] = value
         return data

main.py

@@ -8,9 +8,6 @@ from shelly_pro_3m import ShellyPro3m
 from energysystem import EnergySystem
 from sg_ready_controller import SgReadyController
 
-# For dev-System run in terminal: ssh -N -L 127.0.0.1:8111:10.0.0.10:502 pi@192.168.1.146
-# For productive-System change IP-adress in heatpump to '10.0.0.10' and port to 502
-
 interval_seconds = 10
 
 es = EnergySystem()
@@ -19,7 +16,7 @@ db = DataBaseInflux(
     url="http://192.168.1.146:8086",
     token="Cw_naEZyvJ3isiAh1P4Eq3TsjcHmzzDFS7SlbKDsS6ZWL04fMEYixWqtNxGThDdG27S9aW5g7FP9eiq5z1rsGA==",
     org="allmende",
-    bucket="allmende_db"
+    bucket="allmende_db_v3"
 )
 
 hp_master = HeatPump(device_name='hp_master', ip_address='10.0.0.10', port=502)
@@ -31,6 +28,7 @@ meter = SolaredgeMeter(device_name='solaredge_meter', ip_address='192.168.1.112'
 es.add_components(hp_master, hp_slave, shelly, wr, meter)
 controller = SgReadyController(es)
 
+now = datetime.now()
 while True:
     now = datetime.now()
     if now.second % interval_seconds == 0 and now.microsecond < 100_000:
@@ -42,5 +40,6 @@ while True:
         else:
             mode_as_binary = 1
         db.store_data('sg_ready', {'mode': mode_as_binary})
+
     time.sleep(0.1)
 

requirements.txt

@@ -2,3 +2,4 @@ pymodbus~=3.8.6
 pandas
 openpyxl
 sshtunnel
+pvlib

simulators/pv_plant_simulator.py

@@ -0,0 +1,210 @@
+from __future__ import annotations
+from dataclasses import dataclass
+from typing import Optional, Dict, List, Literal, Tuple, Union
+
+import numpy as np
+import pandas as pd
+import pvlib
+import matplotlib.pyplot as plt
+from pvlib.location import Location
+from pvlib.pvsystem import PVSystem
+from pvlib.modelchain import ModelChain
+
+SeriesOrArray = Union[pd.Series, np.ndarray]
+
+# ----------------------------- Konfiguration -----------------------------
+
+@dataclass
+class PvWattsSubarrayConfig:
+    name: str
+    pdc0_w: float                   # STC-DC-Leistung [W]
+    tilt_deg: float                 # Neigung (0=horizontal)
+    azimuth_deg: float              # Azimut (180=Süd)
+    gamma_pdc: float = -0.004       # Tempkoeff. [1/K]
+    eta_inv_nom: float = 0.96       # WR-Wirkungsgrad (nominal)
+    albedo: float = 0.2             # Bodenreflexion
+
+    # Pauschale Verluste (PVWatts-Losses)
+    dc_loss: float = 0.0
+    ac_loss: float = 0.0
+    soiling: float = 0.0
+
+    # Modell
+    transposition_model: Literal["perez","haydavies","isotropic","klucher","reindl"] = "perez"
+
+
+# ------------------------------ Subarray ---------------------------------
+
+class PvWattsSubarray:
+    """
+    Ein Subarray mit pvlib.ModelChain (PVWatts).
+    Berechnet automatisch DNI/DHI aus GHI (ERBS-Methode)
+    und nutzt ein SAPM-Temperaturmodell.
+    """
+    def __init__(self, cfg: PvWattsSubarrayConfig, location: Location):
+        self.cfg = cfg
+        self.location = location
+        self._mc: Optional[ModelChain] = None
+
+    # ---------------------------------------------------------------------
+    def _create_modelchain(self) -> ModelChain:
+        """Erzeuge eine pvlib.ModelChain-Instanz mit PVWatts-Parametern."""
+        temp_params = pvlib.temperature.TEMPERATURE_MODEL_PARAMETERS["sapm"]["open_rack_glass_polymer"]
+
+        system = PVSystem(
+            surface_tilt=self.cfg.tilt_deg,
+            surface_azimuth=self.cfg.azimuth_deg,
+            module_parameters={"pdc0": self.cfg.pdc0_w, "gamma_pdc": self.cfg.gamma_pdc},
+            inverter_parameters={"pdc0": self.cfg.pdc0_w, "eta_inv_nom": self.cfg.eta_inv_nom},
+            albedo=self.cfg.albedo,
+            temperature_model_parameters=temp_params,
+            module_type="glass_polymer",
+            racking_model="open_rack",
+        )
+
+        mc = ModelChain(
+            system, self.location,
+            transposition_model=self.cfg.transposition_model,
+            solar_position_method="nrel_numpy",
+            airmass_model="kastenyoung1989",
+            dc_model="pvwatts",
+            ac_model="pvwatts",
+            aoi_model="physical",
+            spectral_model=None,
+            losses_model="pvwatts",
+            temperature_model="sapm",
+        )
+
+        mc.losses_parameters = {
+            "dc_loss": float(self.cfg.dc_loss),
+            "ac_loss": float(self.cfg.ac_loss),
+            "soiling": float(self.cfg.soiling),
+        }
+
+        self._mc = mc
+        return mc
+
+    # ---------------------------------------------------------------------
+    def calc_dni_and_dhi(self, weather: pd.DataFrame) -> pd.DataFrame:
+        """
+        Berechnet DNI & DHI aus GHI über die ERBS-Methode.
+        Gibt ein neues DataFrame mit 'ghi', 'dni', 'dhi' zurück.
+        """
+        if "ghi" not in weather:
+            raise ValueError("Wetterdaten benötigen mindestens 'ghi'.")
+        # Sonnenstand bestimmen
+        sp = self.location.get_solarposition(weather.index)
+        erbs = pvlib.irradiance.erbs(weather["ghi"], sp["zenith"], weather.index)
+        out = weather.copy()
+        out["dni"] = erbs["dni"].clip(lower=0)
+        out["dhi"] = erbs["dhi"].clip(lower=0)
+        return out
+
+    # ---------------------------------------------------------------------
+    def _prepare_weather(self, weather: pd.DataFrame) -> pd.DataFrame:
+        """Sichert vollständige Spalten (ghi, dni, dhi, temp_air, wind_speed)."""
+        if "ghi" not in weather or "temp_air" not in weather:
+            raise ValueError("weather benötigt Spalten: 'ghi' und 'temp_air'.")
+
+        w = weather.copy()
+
+        # Zeitzone prüfen
+        if w.index.tz is None:
+            w.index = w.index.tz_localize(self.location.tz)
+        else:
+            if str(w.index.tz) != str(self.location.tz):
+                w = w.tz_convert(self.location.tz)
+
+        # Wind default
+        if "wind_speed" not in w:
+            w["wind_speed"] = 1.0
+
+        # DNI/DHI ergänzen (immer mit ERBS)
+        if "dni" not in w or "dhi" not in w:
+            w = self.calc_dni_and_dhi(w)
+
+        return w
+
+    # ---------------------------------------------------------------------
+    def get_power(self, weather: pd.DataFrame) -> pd.Series:
+        """
+        Berechnet AC-Leistung aus Wetterdaten.
+        """
+        w = self._prepare_weather(weather)
+        mc = self._create_modelchain()
+        mc.run_model(weather=w)
+        return mc.results.ac.rename(self.cfg.name)
+
+
+# ------------------------------- Anlage ----------------------------------
+
+class PvWattsPlant:
+    """
+    Eine PV-Anlage mit mehreren Subarrays, die ein gemeinsames Wetter-DataFrame nutzt.
+    """
+    def __init__(self, site: Location, subarray_cfgs: List[PvWattsSubarrayConfig]):
+        self.site = site
+        self.subs: Dict[str, PvWattsSubarray] = {c.name: PvWattsSubarray(c, site) for c in subarray_cfgs}
+
+    def get_power(
+        self,
+        weather: pd.DataFrame,
+        *,
+        return_breakdown: bool = False
+    ) -> pd.Series | Tuple[pd.Series, Dict[str, pd.Series]]:
+        """Berechne Gesamtleistung und optional Einzel-Subarrays."""
+        parts: Dict[str, pd.Series] = {name: sub.get_power(weather) for name, sub in self.subs.items()}
+
+        # gemeinsamen Index bilden
+        idx = list(parts.values())[0].index
+        for s in parts.values():
+            idx = idx.intersection(s.index)
+        parts = {k: v.reindex(idx).fillna(0.0) for k, v in parts.items()}
+
+        total = sum(parts.values())
+        total.name = "total_ac"
+
+        if return_breakdown:
+            return total, parts
+        return total
+
+
+# --------------------------- Beispielnutzung -----------------------------
+if __name__ == "__main__":
+    # Standort
+    site = Location(latitude=52.52, longitude=13.405, altitude=35, tz="Europe/Berlin", name="Berlin")
+
+    # Zeitachse: 1 Tag, 15-minütig
+    times = pd.date_range("2025-06-21 00:00", "2025-06-21 23:45", freq="15min", tz=site.tz)
+
+    # Dummy-Wetter
+    ghi = 1000 * np.clip(np.sin(np.linspace(0, np.pi, len(times)))**1.2, 0, None)
+    temp_air = 16 + 8 * np.clip(np.sin(np.linspace(-np.pi/2, np.pi/2, len(times))), 0, None)
+    wind = np.full(len(times), 1.0)
+    weather = pd.DataFrame(index=times)
+    weather["ghi"] = ghi
+    weather["temp_air"] = temp_air
+    weather["wind_speed"] = wind
+
+    # Zwei Subarrays
+    cfgs = [
+        PvWattsSubarrayConfig(name="Sued_30", pdc0_w=6000, tilt_deg=30, azimuth_deg=180, dc_loss=0.02, ac_loss=0.01),
+        PvWattsSubarrayConfig(name="West_20", pdc0_w=4000, tilt_deg=20, azimuth_deg=270, soiling=0.02),
+    ]
+    plant = PvWattsPlant(site, cfgs)
+
+    # Simulation
+    total, parts = plant.get_power(weather, return_breakdown=True)
+
+    # Plot
+    plt.figure(figsize=(10, 6))
+    plt.plot(total.index, total / 1000, label="Gesamtleistung (AC)", linewidth=2, color="black")
+    for name, s in parts.items():
+        plt.plot(s.index, s / 1000, label=name)
+    plt.title("PV-Leistung (PVWatts, ERBS-Methode für DNI/DHI)")
+    plt.ylabel("Leistung [kW]")
+    plt.xlabel("Zeit")
+    plt.legend()
+    plt.grid(True, linestyle="--", alpha=0.5)
+    plt.tight_layout()
+    plt.show()