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 +1,62 @@
+import datetime
+import numpy as np
+import pandas as pd
+from sklearn.ensemble import RandomForestRegressor
+
+from methods.common import load_model, save_model
+
+FORECAST_ID = 21
+FEATURES = ["temp_c", "hour_cos", "pv_24h_ago"]
+
+
+def _feature_frame(df):
+    out = df.copy()
+    if "temp_c" not in out.columns:
+        out["temp_c"] = 15.0
+    out["temp_c"] = pd.to_numeric(out["temp_c"], errors="coerce").ffill().bfill().fillna(15.0)
+    out["hour_float"] = out.index.hour + out.index.minute / 60.0
+    out["hour_cos"] = np.cos(2 * np.pi * out["hour_float"] / 24.0)
+    if "PV" in out.columns:
+        out["pv_24h_ago"] = out["PV"].shift(288)
+    return out
+
+
+def train(data_obj):
+    aid = data_obj["config"]["anlagen_id"]
+    df = _feature_frame(data_obj.get("df_pv_training", data_obj["df_hist"]).copy())
+    if "PV" not in df.columns:
+        return {"trained": False, "reason": "PV fehlt"}
+    df = df.dropna(subset=FEATURES + ["PV"])
+    if len(df) < 288:
+        return {"trained": False, "reason": "zu wenig Daten", "samples": int(len(df))}
+
+    last_day = df.index.max().normalize()
+    train_df = df[df.index < last_day]
+    test_df = df[(df.index >= last_day) & (df.index < last_day + datetime.timedelta(days=1))]
+    score = None
+    if len(train_df) >= 288 and len(test_df) >= 12:
+        eval_model = RandomForestRegressor(n_estimators=300, max_depth=15, random_state=42)
+        eval_model.fit(train_df[FEATURES], train_df["PV"])
+        score = float(eval_model.score(test_df[FEATURES], test_df["PV"]))
+        print(f"[var_21] R2 PV letzter kompletter Tag: {score:.3f}")
+
+    model = RandomForestRegressor(n_estimators=300, max_depth=15, random_state=42)
+    model.fit(df[FEATURES], df["PV"])
+    path = save_model(aid, FORECAST_ID, {"model": model, "features": FEATURES, "r2_last_day": score})
+    return {"trained": True, "samples": int(len(df)), "features": FEATURES, "r2_last_day": score, "path": path}
+
+
+def predict(data_obj):
+    aid = data_obj["config"]["anlagen_id"]
+    artifact = load_model(aid, FORECAST_ID)
+    model = artifact["model"] if artifact and "model" in artifact else None
+    hist = data_obj["df_hist"]
+    fut = data_obj["df_fut"].copy()
+    res = {}
+    for t in fut.index:
+        t_24 = t - datetime.timedelta(days=1)
+        pv_24 = float(hist.at[t_24, "PV"]) if t_24 in hist.index else 0.0
+        row = pd.DataFrame([[float(fut.at[t, "temp_c"]), float(fut.at[t, "hour_cos"]), pv_24]], columns=FEATURES)
+        pred = float(model.predict(row)[0]) if model is not None else pv_24
+        res[t] = max(0.0, pred)
+    return res