feat(application): integrate measured-load ingestion training and planner source

2026-10-02 21:00:05 +00:00
parent 44362e4bf5
commit fc024fcfba
96 changed files with 8933 additions and 0 deletions
@@ -0,0 +1,66 @@
+import numpy as np
+import pandas as pd
+from sklearn.ensemble import RandomForestRegressor
+
+from methods.common import load_model, save_model
+from shared_utils import calc_pure_math_pv, roof_features
+
+FORECAST_ID = 1
+FEATURES = [
+    "math_pv",
+    "temp_c",
+    "cloud",
+    "hour_sin",
+    "hour_cos",
+    "sin_year",
+    "cos_year",
+    "pv_kwp_total",
+    "roof_azimuth_sin",
+    "roof_azimuth_cos",
+    "roof_tilt_avg",
+    "roof_south_factor",
+]
+
+
+def _features(frame, config):
+    out = frame.copy()
+    out["math_pv"] = [calc_pure_math_pv(config, t) for t in out.index]
+    rf = roof_features(config)
+    for k, v in rf.items():
+        out[k] = v
+    for col, default in [("temp_c", 15.0), ("cloud", 20.0)]:
+        if col not in out.columns:
+            out[col] = default
+        out[col] = pd.to_numeric(out[col], errors="coerce").ffill().bfill().fillna(default)
+    return out[FEATURES].astype(float)
+
+
+def train(data_obj):
+    config = data_obj["config"]
+    aid = config["anlagen_id"]
+    df = data_obj.get("df_pv_training", data_obj["df_hist"]).copy()
+    if "PV" not in df.columns:
+        return {"trained": False, "reason": "PV fehlt"}
+    X = _features(df, config)
+    y = pd.to_numeric(df["PV"], errors="coerce")
+    valid = X.notna().all(axis=1) & y.notna()
+    X, y = X.loc[valid], y.loc[valid]
+    if len(X) < 288:
+        return {"trained": False, "reason": "zu wenig Daten", "samples": int(len(X))}
+    model = RandomForestRegressor(n_estimators=400, max_depth=18, min_samples_leaf=2, random_state=42, n_jobs=-1)
+    model.fit(X, y)
+    path = save_model(aid, FORECAST_ID, {"model": model, "features": FEATURES})
+    return {"trained": True, "samples": int(len(X)), "path": path}
+
+
+def predict(data_obj):
+    config = data_obj["config"]
+    aid = config["anlagen_id"]
+    artifact = load_model(aid, FORECAST_ID)
+    X = _features(data_obj["df_fut"], config)
+    if artifact and "model" in artifact:
+        values = artifact["model"].predict(X)
+    else:
+        values = X["math_pv"].to_numpy()
+    ac_limit = float(config.get("ac_leistung", 10.0) or 10.0) * 1000.0
+    return {t: max(0.0, min(float(v), ac_limit)) for t, v in zip(data_obj["df_fut"].index, values)}