läuft noch nicht; slave inverter liest in komischen zeitabständen und es gibt wohl bei einigen Registereinträgen probleme

forecaster/load_forecaster.py

@@ -1,349 +0,0 @@
-# load_forecaster.py
-# -*- coding: utf-8 -*-
-"""
-LoadForecaster: builds a 36-hour forecast at 15-min resolution from InfluxDB data.
-
-- Data source: InfluxDB (Flux query provided by user)
-- Target: House load = M_AC_real - I_AC_real
-- Frequency: 15 minutes (changeable via init)
-- Model: Keras (LSTM by default, pluggable)
-- Persistence: Saves model (H5) and scaler (joblib)
-
-Usage (example):
-
-    from load_forecaster import LoadForecaster
-    import tensorflow as tf
-
-    lf = LoadForecaster(
-        url="http://localhost:8086",
-        token="<YOUR_TOKEN>",
-        org="<YOUR_ORG>",
-        bucket="allmende_db",
-        agg_every="15m",
-        input_hours=72,
-        output_hours=36,
-        model_path="model/load_forecaster.h5",
-        scaler_path="model/scaler.joblib",
-    )
-
-    # Train or retrain
-    lf.train_and_save(train_days=90, epochs=60)
-
-    # Load model and forecast
-    model = lf.load_model()
-    forecast_df = lf.get_15min_forecast(model)
-    print(forecast_df.head())
-
-"""
-from __future__ import annotations
-import os
-import math
-import json
-import warnings
-from dataclasses import dataclass
-from typing import Optional, Tuple
-
-import numpy as np
-import pandas as pd
-from influxdb_client import InfluxDBClient
-from influxdb_client.client.warnings import MissingPivotFunction
-from sklearn.preprocessing import StandardScaler
-from sklearn.exceptions import NotFittedError
-import joblib
-
-# TensorFlow / Keras
-import tensorflow as tf
-from tensorflow.keras.models import Sequential, load_model
-from tensorflow.keras.layers import LSTM, Dense, Dropout
-from tensorflow.keras.callbacks import EarlyStopping
-
-warnings.filterwarnings("ignore", category=MissingPivotFunction)
-
-@dataclass
-class InfluxParams:
-    url: str
-    token: str
-    org: str
-    bucket: str = "allmende_db"
-
-class LoadForecaster:
-    def __init__(
-        self,
-        url: str,
-        token: str,
-        org: str,
-        bucket: str = "allmende_db",
-        agg_every: str = "15m",
-        input_hours: int = 72,
-        output_hours: int = 36,
-        model_path: str = "model/load_forecaster.h5",
-        scaler_path: str = "model/scaler.joblib",
-        feature_config: Optional[dict] = None,
-    ) -> None:
-        self.influx = InfluxParams(url=url, token=token, org=org, bucket=bucket)
-        self.agg_every = agg_every
-        self.input_steps = int((input_hours * 60) / self._freq_minutes(agg_every))
-        self.output_steps = int((output_hours * 60) / self._freq_minutes(agg_every))
-        self.model_path = model_path
-        self.scaler_path = scaler_path
-        self.feature_config = feature_config or {"use_temp": True, "use_time_cyc": True}
-        self._scaler: Optional[StandardScaler] = None
-
-        # Ensure model dir exists
-        os.makedirs(os.path.dirname(model_path), exist_ok=True)
-
-    # ---------------------------- Public API ---------------------------- #
-    def get_15min_forecast(self, model: tf.keras.Model) -> pd.DataFrame:
-        """Create a 36-hour forecast at 15-min resolution using the latest data.
-        Assumes a StandardScaler has been fitted during training and saved.
-        The method uses the most recent input window from InfluxDB.
-        """
-        # Pull just enough history for one input window
-        history_hours = math.ceil(self.input_steps * self._freq_minutes(self.agg_every) / 60)
-        df = self._query_and_prepare(range_hours=history_hours)
-        if len(df) < self.input_steps:
-            raise RuntimeError(f"Not enough data: need {self.input_steps} steps, got {len(df)}")
-
-        # Build features for the latest window
-        feats = self._build_features(df)
-        X_window = feats[-self.input_steps :]
-
-        # Load scaler
-        scaler = self._load_or_get_scaler()
-        X_scaled = scaler.transform(X_window)
-
-        # Predict
-        pred_scaled = model.predict(X_scaled[np.newaxis, ...], verbose=0)[0]
-
-        # Inverse transform only the target column (index 0 is Load)
-        # Reconstruct a full array to inverse_transform
-        inv = np.zeros((self.output_steps, X_scaled.shape[1]))
-        inv[:, 0] = pred_scaled
-        inv_full = scaler.inverse_transform(inv)
-        y_pred = inv_full[:, 0]
-
-        # Build forecast index
-        last_ts = df.index[-1]
-        freq = pd.tseries.frequencies.to_offset(self.agg_every)
-        idx = pd.date_range(last_ts + freq, periods=self.output_steps, freq=freq)
-        out = pd.DataFrame({"Forecast_Load": y_pred}, index=idx)
-        out.index.name = "timestamp"
-        return out
-
-    def train_and_save(
-        self,
-        train_days: int = 90,
-        epochs: int = 80,
-        batch_size: int = 128,
-        validation_split: float = 0.2,
-        learning_rate: float = 1e-3,
-        fine_tune: bool = False,
-    ) -> tf.keras.Model:
-        """Train (or fine-tune) a model from recent history and persist model + scaler."""
-        df = self._query_and_prepare(range_hours=24 * train_days)
-        feats = self._build_features(df)
-
-        # Prepare windows
-        X, y = self._make_windows(feats)
-        if len(X) < 10:
-            raise RuntimeError("Not enough windowed samples to train.")
-
-        # Fit scaler on full X
-        scaler = StandardScaler()
-        X_scaled = scaler.fit_transform(X)
-        self._scaler = scaler
-        joblib.dump(scaler, self.scaler_path)
-
-        # Build model (or load existing for fine-tune)
-        if fine_tune and os.path.exists(self.model_path):
-            model = load_model(self.model_path)
-        else:
-            model = self._build_default_model(input_dim=X.shape[1], output_dim=self.output_steps, lr=learning_rate)
-
-        # Train
-        es = EarlyStopping(monitor="val_loss", patience=10, restore_best_weights=True)
-        model.fit(
-            X_scaled.reshape((-1, self.input_steps, X.shape[1] // self.input_steps)),
-            y,
-            epochs=epochs,
-            batch_size=batch_size,
-            validation_split=validation_split,
-            callbacks=[es],
-            verbose=1,
-        )
-
-        model.save(self.model_path)
-        return model
-
-    # A convenience wrapper to be called from an external script once per day
-    def retrain_daily(self, train_days: int = 90, epochs: int = 40, fine_tune: bool = True) -> None:
-        self.train_and_save(train_days=train_days, epochs=epochs, fine_tune=fine_tune)
-
-    def load_model(self) -> tf.keras.Model:
-        if not os.path.exists(self.model_path):
-            raise FileNotFoundError(f"Model not found at {self.model_path}")
-        return load_model(self.model_path)
-
-    # ------------------------- Internals: Data ------------------------- #
-    def _query_and_prepare(self, range_hours: int) -> pd.DataFrame:
-        """Query InfluxDB for the last `range_hours` and construct the Load series.
-        Expected fields (exactly as in DB):
-          - "40206 - M_AC_Power"
-          - "40210 - M_AC_Power_SF"
-          - "40083 - I_AC_Power"
-          - "40084 - I_AC_Power_SF"
-          - "300 - Aussentemperatur"
-        """
-        start_str = f"-{range_hours}h"
-        flux = f'''
-from(bucket: "{self.influx.bucket}")
-  |> range(start: {start_str})
-  |> filter(fn: (r) => r["_measurement"] == "solaredge_meter" or r["_measurement"] == "solaredge_master" or r["_measurement"] == "hp_master")
-  |> filter(fn: (r) => r["_field"] == "40206 - M_AC_Power" or r["_field"] == "40210 - M_AC_Power_SF" or r["_field"] == "40083 - I_AC_Power" or r["_field"] == "40084 - I_AC_Power_SF" or r["_field"] == "300 - Aussentemperatur")
-  |> aggregateWindow(every: {self.agg_every}, fn: mean, createEmpty: false)
-  |> yield(name: "mean")
-'''
-        with InfluxDBClient(url=self.influx.url, token=self.influx.token, org=self.influx.org) as client:
-            tables = client.query_api().query_data_frame(flux)
-
-        # Concatenate if list of frames is returned
-        if isinstance(tables, list):
-            df = pd.concat(tables, ignore_index=True)
-        else:
-            df = tables
-
-        # Keep relevant columns and pivot
-        df = df[["_time", "_field", "_value"]]
-        df = df.pivot(index="_time", columns="_field", values="_value").reset_index()
-        df = df.rename(
-            columns={
-                "_time": "timestamp",
-                "40206 - M_AC_Power": "M_AC",
-                "40210 - M_AC_Power_SF": "M_SF",
-                "40083 - I_AC_Power": "I_AC",
-                "40084 - I_AC_Power_SF": "I_SF",
-                "300 - Aussentemperatur": "Temp",
-            }
-        )
-        df = df.sort_values("timestamp").set_index("timestamp")
-
-        # Forward-fill reasonable gaps (e.g., scaler factors and temp)
-        df[["M_SF", "I_SF", "Temp"]] = df[["M_SF", "I_SF", "Temp"]].ffill()
-
-        # Apply scaling: real = value * 10^sf
-        df["I_AC_real"] = df["I_AC"] * np.power(10.0, df["I_SF"]).astype(float)
-        df["M_AC_real"] = df["M_AC"] * np.power(10.0, df["M_SF"]).astype(float)
-
-        # Compute load
-        df["Load"] = df["M_AC_real"] - df["I_AC_real"]
-
-        # Ensure regular 15-min grid
-        df = df.asfreq(self.agg_every)
-        df[["Load", "Temp"]] = df[["Load", "Temp"]].interpolate(limit_direction="both")
-
-        return df[["Load", "Temp"]]
-
-    def _build_features(self, df: pd.DataFrame) -> np.ndarray:
-        """Create feature matrix: [Load, Temp?, sin/cos day, sin/cos dow]."""
-        feats = [df["Load"].values.reshape(-1, 1)]
-
-        if self.feature_config.get("use_temp", True):
-            feats.append(df["Temp"].values.reshape(-1, 1))
-
-        if self.feature_config.get("use_time_cyc", True):
-            idx = df.index
-            minute_of_day = (idx.hour * 60 + idx.minute).values.astype(float)
-            sod = 2 * np.pi * minute_of_day / (24 * 60)
-            dow = 2 * np.pi * idx.dayofweek.values.astype(float) / 7.0
-            feats.append(np.sin(sod).reshape(-1, 1))
-            feats.append(np.cos(sod).reshape(-1, 1))
-            feats.append(np.sin(dow).reshape(-1, 1))
-            feats.append(np.cos(dow).reshape(-1, 1))
-
-        X = np.hstack(feats)  # shape: (T, n_features)
-
-        # Flatten windows to 2D for scaler fitting, but model expects 3D; we reshape later
-        return X
-
-    def _make_windows(self, X_2d: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
-        """Create sliding windows: returns (X_flat, y) where X_flat stacks the windowed features.
-        For Keras we later reshape X_flat -> (N, input_steps, n_features).
-        """
-        n = X_2d.shape[0]
-        n_features = X_2d.shape[1]
-        X_list, y_list = [], []
-        for i in range(n - self.input_steps - self.output_steps):
-            xw = X_2d[i : i + self.input_steps, :]
-            yw = X_2d[i + self.input_steps : i + self.input_steps + self.output_steps, 0]  # target: Load
-            X_list.append(xw.reshape(-1))  # flatten
-            y_list.append(yw)
-        X_flat = np.stack(X_list)
-        y = np.stack(y_list)
-        return X_flat, y
-
-    # ----------------------- Internals: Modeling ----------------------- #
-    def _build_default_model(self, input_dim: int, output_dim: int, lr: float = 1e-3) -> tf.keras.Model:
-        n_features = input_dim // self.input_steps
-        model = Sequential([
-            LSTM(96, input_shape=(self.input_steps, n_features), return_sequences=False),
-            Dropout(0.1),
-            Dense(output_dim)
-        ])
-        model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=lr), loss="mse")
-        return model
-
-    def _load_or_get_scaler(self) -> StandardScaler:
-        if self._scaler is not None:
-            return self._scaler
-        if not os.path.exists(self.scaler_path):
-            raise NotFittedError("Scaler not found. Train the model first to create scaler.")
-        self._scaler = joblib.load(self.scaler_path)
-        return self._scaler
-
-    @staticmethod
-    def _freq_minutes(spec: str) -> int:
-        # supports formats like "15m", "1h"
-        if spec.endswith("m"):
-            return int(spec[:-1])
-        if spec.endswith("h"):
-            return int(spec[:-1]) * 60
-        raise ValueError(f"Unsupported frequency spec: {spec}")
-
-
-# ----------------------------- retrain_daily.py -----------------------------
-# A tiny script you can run once per day (e.g., via cron/systemd) to retrain the model.
-# It delegates the work to LoadForecaster.retrain_daily().
-
-if __name__ == "__main__":
-    # Read credentials/config from env vars or fill here
-    URL = os.getenv("INFLUX_URL", "http://localhost:8086")
-    TOKEN = os.getenv("INFLUX_TOKEN", "<YOUR_TOKEN>")
-    ORG = os.getenv("INFLUX_ORG", "<YOUR_ORG>")
-    BUCKET = os.getenv("INFLUX_BUCKET", "allmende_db")
-
-    lf = LoadForecaster(
-        url=URL,
-        token=TOKEN,
-        org=ORG,
-        bucket=BUCKET,
-        agg_every="15m",
-        input_hours=72,
-        output_hours=36,
-        model_path=os.getenv("FORECASTER_MODEL", "model/load_forecaster.h5"),
-        scaler_path=os.getenv("FORECASTER_SCALER", "model/scaler.joblib"),
-    )
-
-    # One call per day is enough; decrease epochs for faster daily updates
-    lf.retrain_daily(train_days=int(os.getenv("TRAIN_DAYS", "120")), epochs=int(os.getenv("EPOCHS", "30")), fine_tune=True)
-
-    # Optionally, produce a fresh forecast right after training
-    try:
-        model = lf.load_model()
-        fc = lf.get_15min_forecast(model)
-        # Save latest forecast to CSV for dashboards/consumers
-        out_path = os.getenv("FORECAST_OUT", "model/latest_forecast_15min.csv")
-        os.makedirs(os.path.dirname(out_path), exist_ok=True)
-        fc.to_csv(out_path)
-        print(f"Saved forecast: {out_path}")
-    except Exception as e:
-        print(f"Forecast generation failed: {e}")

main.py

@@ -26,56 +26,57 @@ db = DataBaseInflux(
     bucket="allmende_db"
 )
 
-hp_master = HeatPump(device_name='hp_master', ip_address='10.0.0.10', port=502)
-hp_slave = HeatPump(device_name='hp_slave', ip_address='10.0.0.11', port=502)
-shelly = ShellyPro3m(device_name='wohnung_2_6', ip_address='192.168.1.121')
-wr = PvInverter(device_name='solaredge_master', ip_address='192.168.1.112')
+# hp_master = HeatPump(device_name='hp_master', ip_address='10.0.0.10', port=502)
+# hp_slave = HeatPump(device_name='hp_slave', ip_address='10.0.0.11', port=502)
+# shelly = ShellyPro3m(device_name='wohnung_2_6', ip_address='192.168.1.121')
+wr_master = PvInverter(device_name='solaredge_master', ip_address='192.168.1.112', unit=1)
+wr_slave = PvInverter(device_name='solaredge_slave', ip_address='192.168.1.112', unit=3)
 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)
-
-# FORECASTING
-latitude = 48.041
-longitude = 7.862
-TZ = "Europe/Berlin"
-HORIZON_DAYS = 2
-weather_forecaster = WeatherForecaster(latitude=latitude, longitude=longitude)
-site = Location(latitude=latitude, longitude=longitude, altitude=35, tz=TZ, name="Gundelfingen")
-
-p_module = 435
-upper_roof_north = PvWattsSubarrayConfig(name="north", pdc0_w=(29+29+21)*p_module, tilt_deg=10, azimuth_deg=20, dc_loss=0.02, ac_loss=0.01)
-upper_roof_south = PvWattsSubarrayConfig(name="south", pdc0_w=(29+21+20)*p_module, tilt_deg=10, azimuth_deg=200, dc_loss=0.02, ac_loss=0.01)
-upper_roof_east = PvWattsSubarrayConfig(name="east", pdc0_w=7*p_module, tilt_deg=10, azimuth_deg=110, dc_loss=0.02, ac_loss=0.01)
-upper_roof_west = PvWattsSubarrayConfig(name="west", pdc0_w=7*p_module, tilt_deg=10, azimuth_deg=290, dc_loss=0.02, ac_loss=0.01)
-cfgs = [upper_roof_north, upper_roof_south, upper_roof_east, upper_roof_west]
-pv_plant = PvWattsPlant(site, cfgs)
-
-now = datetime.now()
-next_forecast_at = (now + dt.timedelta(hours=1)).replace(minute=0, second=0, microsecond=0)
+es.add_components(wr_master, wr_slave)#hp_master, hp_slave, shelly, wr_master, wr_slave, meter)
+# controller = SgReadyController(es)
+#
+# # FORECASTING
+# latitude = 48.041
+# longitude = 7.862
+# TZ = "Europe/Berlin"
+# HORIZON_DAYS = 2
+# weather_forecaster = WeatherForecaster(latitude=latitude, longitude=longitude)
+# site = Location(latitude=latitude, longitude=longitude, altitude=35, tz=TZ, name="Gundelfingen")
+#
+# p_module = 435
+# upper_roof_north = PvWattsSubarrayConfig(name="north", pdc0_w=(29+29+21)*p_module, tilt_deg=10, azimuth_deg=20, dc_loss=0.02, ac_loss=0.01)
+# upper_roof_south = PvWattsSubarrayConfig(name="south", pdc0_w=(29+21+20)*p_module, tilt_deg=10, azimuth_deg=200, dc_loss=0.02, ac_loss=0.01)
+# upper_roof_east = PvWattsSubarrayConfig(name="east", pdc0_w=7*p_module, tilt_deg=10, azimuth_deg=110, dc_loss=0.02, ac_loss=0.01)
+# upper_roof_west = PvWattsSubarrayConfig(name="west", pdc0_w=7*p_module, tilt_deg=10, azimuth_deg=290, dc_loss=0.02, ac_loss=0.01)
+# cfgs = [upper_roof_north, upper_roof_south, upper_roof_east, upper_roof_west]
+# pv_plant = PvWattsPlant(site, cfgs)
+#
+# now = datetime.now()
+# next_forecast_at = (now + dt.timedelta(hours=1)).replace(minute=0, second=0, microsecond=0)
 while True:
     now = datetime.now()
     if now.second % interval_seconds == 0 and now.microsecond < 100_000:
         state = es.get_state_and_store_to_database(db)
-        mode = controller.perform_action(heat_pump_name='hp_master', meter_name='solaredge_meter', state=state)
+        # mode = controller.perform_action(heat_pump_name='hp_master', meter_name='solaredge_meter', state=state)
+        #
+        # if mode == 'mode1':
+        #     mode_as_binary = 0
+        # else:
+        #     mode_as_binary = 1
+        # db.store_data('sg_ready', {'mode': mode_as_binary})
 
-        if mode == 'mode1':
-            mode_as_binary = 0
-        else:
-            mode_as_binary = 1
-        db.store_data('sg_ready', {'mode': mode_as_binary})
-
-    if now >= next_forecast_at:
-        # Start der Prognose: ab der kommenden vollen Stunde
-        start_hour_local = (now + dt.timedelta(hours=1)).replace(minute=0, second=0, microsecond=0)
-        weather = weather_forecaster.get_hourly_forecast(start_hour_local, HORIZON_DAYS)
-        total = pv_plant.get_power(weather)
-        db.store_forecasts('pv_forecast', total)
-
-        # Nächste geplante Ausführung definieren (immer volle Stunde)
-        # Falls wir durch Delay mehrere Stunden verpasst haben, hole auf:
-        while next_forecast_at <= now:
-            next_forecast_at = (next_forecast_at + dt.timedelta(hours=1)).replace(minute=0, second=0, microsecond=0)
+    # if now >= next_forecast_at:
+    #     # Start der Prognose: ab der kommenden vollen Stunde
+    #     start_hour_local = (now + dt.timedelta(hours=1)).replace(minute=0, second=0, microsecond=0)
+    #     weather = weather_forecaster.get_hourly_forecast(start_hour_local, HORIZON_DAYS)
+    #     total = pv_plant.get_power(weather)
+    #     db.store_forecasts('pv_forecast', total)
+    #
+    #     # Nächste geplante Ausführung definieren (immer volle Stunde)
+    #     # Falls wir durch Delay mehrere Stunden verpasst haben, hole auf:
+    #     while next_forecast_at <= now:
+    #         next_forecast_at = (next_forecast_at + dt.timedelta(hours=1)).replace(minute=0, second=0, microsecond=0)
 
 
     time.sleep(0.1)

pv_inverter.py

@@ -1,139 +1,155 @@
-import time
-import struct
-import pandas as pd
-from typing import Dict, Any, List, Tuple, Optional
+# pv_inverter.py
+# -*- coding: utf-8 -*-
+from typing import Optional, Dict, Any, List
 from pymodbus.client import ModbusTcpClient
+from pymodbus.exceptions import ModbusIOException
+import struct
+import time
 
-EXCEL_PATH = "modbus_registers/pv_inverter_registers.xlsx"
-
-# Obergrenze: bis EXKLUSIVE 40206 (d.h. max. 40205)
-MAX_ADDR_EXCLUSIVE = 40121
 
 class PvInverter:
-    def __init__(self, device_name: str, ip_address: str, port: int = 502, unit: int = 1):
-        self.device_name = device_name
-        self.ip = ip_address
-        self.port = port
-        self.unit = unit
-        self.client: Optional[ModbusTcpClient] = None
-        self.registers: Dict[int, Dict[str, Any]] = {}  # addr -> {"desc":..., "type":...}
-        self.connect_to_modbus()
-        self.load_registers(EXCEL_PATH)
+    """
+    Minimaler Reader für einen SolarEdge-Inverter hinter Modbus-TCP→RTU-Gateway.
+    Liest nur die bekannten Register (wie im funktionierenden Skript).
+    Kompatibel mit pymodbus 2.5.x und 3.x – kein retry_on_empty.
+    """
 
-    # ---------- Verbindung ----------
-    def connect_to_modbus(self):
-        self.client = ModbusTcpClient(self.ip, port=self.port, timeout=3.0, retries=3)
+    def __init__(
+        self,
+        device_name: str,
+        ip_address: str,
+        port: int = 502,
+        unit_id: int = 1,
+        timeout: float = 1.5,
+        silent_interval: float = 0.02,
+    ):
+        self.device_name = device_name
+        self.host = ip_address
+        self.port = port
+        self.unit = unit_id
+        self.timeout = timeout
+        self.silent_interval = silent_interval
+        self.client: Optional[ModbusTcpClient] = None
+        self._connect()
+
+    # ---------------- Verbindung ----------------
+    def _connect(self):
+        # retries=0: keine internen Mehrfachversuche
+        self.client = ModbusTcpClient(self.host, port=self.port, timeout=self.timeout, retries=0)
         if not self.client.connect():
-            print("❌ Verbindung zu Wechselrichter fehlgeschlagen.")
-            raise SystemExit(1)
-        print("✅ Verbindung zu Wechselrichter hergestellt.")
+            raise ConnectionError(f"Verbindung zu {self.device_name} ({self.host}:{self.port}) fehlgeschlagen.")
+        print(f"✅ Verbindung hergestellt zu {self.device_name} ({self.host}:{self.port}, unit={self.unit})")
 
     def close(self):
         if self.client:
             self.client.close()
             self.client = None
 
-    # ---------- Register-Liste ----------
-    def load_registers(self, excel_path: str):
-        xls = pd.ExcelFile(excel_path)
-        df = xls.parse()
-        # Passe Spaltennamen hier an, falls nötig:
-        cols = ["MB Adresse", "Beschreibung", "Variabel Typ"]
-        df = df[cols].dropna()
-        df["MB Adresse"] = df["MB Adresse"].astype(int)
-
-        # 1) Vorab-Filter: nur Adressen < 40206 übernehmen
-        df = df[df["MB Adresse"] < MAX_ADDR_EXCLUSIVE]
-
-        self.registers = {
-            int(row["MB Adresse"]): {
-                "desc": str(row["Beschreibung"]).strip(),
-                "type": str(row["Variabel Typ"]).strip()
-            }
-            for _, row in df.iterrows()
-        }
-
-
-    # ---------- Low-Level Lesen ----------
-    def _try_read(self, fn_name: str, address: int, count: int) -> Optional[List[int]]:
-        fn = getattr(self.client, fn_name)
-        # pymodbus 3.8.x hat 'slave='; Fallbacks schaden nicht
-        for kwargs in (dict(address=address, count=count, slave=self.unit),
-                       dict(address=address, count=count)):
+    # ---------------- Low-Level Lesen ----------------
+    def _read_regs(self, addr: int, count: int) -> Optional[List[int]]:
+        """Liest 'count' Holding-Register ab base-0 'addr' für die konfigurierte Unit-ID."""
         try:
-                res = fn(**kwargs)
-                if res is None or (hasattr(res, "isError") and res.isError()):
-                    continue
-                return res.registers
-            except TypeError:
-                continue
+            rr = self.client.read_holding_registers(address=addr, count=count, slave=self.unit)
+        except ModbusIOException:
+            time.sleep(self.silent_interval)
+            return None
+        except Exception:
+            time.sleep(self.silent_interval)
             return None
 
-    def _read_any(self, address: int, count: int) -> Optional[List[int]]:
-        regs = self._try_read("read_holding_registers", address, count)
-        if regs is None:
-            regs = self._try_read("read_input_registers", address, count)
-        return regs
+        time.sleep(self.silent_interval)
+        if not rr or rr.isError():
+            return None
+        return rr.registers
 
-    # ---------- Decoding ----------
     @staticmethod
-    def _to_i16(u16: int) -> int:
+    def _to_int16(u16: int) -> int:
         return struct.unpack(">h", struct.pack(">H", u16))[0]
 
     @staticmethod
-    def _to_f32_from_two(u16_hi: int, u16_lo: int, msw_first: bool = True) -> float:
-        b = struct.pack(">HH", u16_hi, u16_lo) if msw_first else struct.pack(">HH", u16_lo, u16_hi)
-        return struct.unpack(">f", b)[0]
+    def _apply_sf(raw: int, sf: int) -> float:
+        return raw * (10 ** sf)
 
-    # Hilfsfunktion: wie viele 16-Bit-Register braucht dieser Typ?
     @staticmethod
-    def _word_count_for_type(rtype: str) -> int:
-        rt = (rtype or "").lower()
-        # Passe hier an deine Excel-Typen an:
-        if "uint32" in rt or "real" in rt or "float" in rt or "string(32)" in rt:
-            return 2
-        # Default: 1 Wort (z.B. int16/uint16)
-        return 1
+    def _read_string_from_regs(regs: List[int]) -> Optional[str]:
+        b = b"".join(struct.pack(">H", r) for r in regs)
+        s = b.decode("ascii", errors="ignore").rstrip("\x00 ").strip()
+        return s or None
 
-    def read_one(self, address_excel: int, rtype: str) -> Optional[float]:
-        """
-        Liest einen Wert nach Typ ('INT' oder 'REAL' etc.).
-        Es werden ausschließlich Register < 40206 gelesen.
-        """
-        addr = int(address_excel)
-        words = self._word_count_for_type(rtype)
-
-        # 2) Harte Grenze prüfen: höchstes angefasstes Register muss < 40206 sein
-        if addr + words - 1 >= MAX_ADDR_EXCLUSIVE:
-            # Überspringen, da der Lesevorgang die Grenze >= 40206 berühren würde
+    # ---------------- Hilfsfunktionen ----------------
+    def _read_string(self, addr: int, words: int) -> Optional[str]:
+        regs = self._read_regs(addr, words)
+        if regs is None:
             return None
+        return self._read_string_from_regs(regs)
 
-        if words == 2:
-            regs = self._read_any(addr, 2)
-            if not regs or len(regs) < 2:
+    def _read_scaled(self, value_addr: int, sf_addr: int) -> Optional[float]:
+        regs = self._read_regs(value_addr, 1)
+        sf   = self._read_regs(sf_addr, 1)
+        if regs is None or sf is None:
             return None
-            # Deine bisherige Logik interpretiert 2 Worte als Float32:
-            return self._to_f32_from_two(regs[0], regs[1])
-        else:
-            regs = self._read_any(addr, 1)
-            if not regs:
-                return None
-            return float(self._to_i16(regs[0]))
+        raw = self._to_int16(regs[0])
+        sff = self._to_int16(sf[0])
+        return self._apply_sf(raw, sff)
 
+    def _read_u32_with_sf(self, value_addr: int, sf_addr: int) -> Optional[float]:
+        regs = self._read_regs(value_addr, 2)
+        sf   = self._read_regs(sf_addr, 1)
+        if regs is None or sf is None:
+            return None
+        u32 = (regs[0] << 16) | regs[1]
+        sff = self._to_int16(sf[0])
+        return self._apply_sf(u32, sff)
+
+    # ---------------- Öffentliche API ----------------
     def get_state(self) -> Dict[str, Any]:
-        """
-        Liest ALLE Register aus self.registers und gibt dict zurück.
-        Achtet darauf, dass keine Adresse (inkl. Mehrwort) >= 40206 gelesen wird.
-        """
-        data = {"Zeit": time.strftime("%Y-%m-%d %H:%M:%S")}
-        for address, meta in sorted(self.registers.items()):
-            words = self._word_count_for_type(meta["type"])
-            # 3) Nochmals Schutz auf Ebene der Iteration:
-            if address + words - 1 >= MAX_ADDR_EXCLUSIVE:
-                continue
-            val = self.read_one(address, meta["type"])
-            if val is None:
-                continue
-            key = f"{address} - {meta['desc']}"
-            data[key] = val
-        return data
+        """Liest exakt die bekannten Register und gibt ein Dict zurück."""
+        state: Dict[str, Any] = {}
+
+        # --- Common Block ---
+        state["C_Manufacturer"] = self._read_string(40004, 16)
+        state["C_Model"]        = self._read_string(40020, 16)
+        state["C_Version"]      = self._read_string(40044, 8)
+        state["C_SerialNumber"] = self._read_string(40052, 16)
+
+        # --- Inverter Block ---
+        state["I_AC_Power_W"]        = self._read_scaled(40083, 40084)
+        state["I_AC_Voltage_V"]      = self._read_scaled(40079, 40082)
+        state["I_AC_Frequency_Hz"]   = self._read_scaled(40085, 40086)
+        state["I_DC_Power_W"]        = self._read_scaled(40100, 40101)
+        state["I_AC_Energy_Wh_total"] = self._read_u32_with_sf(40093, 40095)
+
+        status_regs = self._read_regs(40107, 2)
+        if status_regs:
+            state["I_Status"] = status_regs[0]
+            state["I_Status_Vendor"] = status_regs[1]
+        else:
+            state["I_Status"] = None
+            state["I_Status_Vendor"] = None
+
+        return state
+
+
+# ---------------- Beispiel ----------------
+if __name__ == "__main__":
+    MODBUS_IP = "192.168.1.112"
+    MODBUS_PORT = 502
+
+    master = PvInverter("solaredge_master", MODBUS_IP, port=MODBUS_PORT, unit_id=1)
+    slave  = PvInverter("solaredge_slave",  MODBUS_IP, port=MODBUS_PORT, unit_id=3)
+
+    try:
+        sm = master.get_state()
+        ss = slave.get_state()
+
+        print("\n=== MASTER ===")
+        for k, v in sm.items():
+            print(f"{k:22s}: {v}")
+
+        print("\n=== SLAVE ===")
+        for k, v in ss.items():
+            print(f"{k:22s}: {v}")
+
+    finally:
+        master.close()
+        slave.close()