feat(application): integrate measured-load ingestion training and planner source
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ENELIX Agent
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import datetime
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import numpy as np
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import pandas as pd
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from sklearn.ensemble import HistGradientBoostingRegressor
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from methods.common import load_model, save_model
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from telemetry_quality import profile_source_value
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FORECAST_ID = 2
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FEATURES = [
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"temp_c",
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"cloud",
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"hour_sin",
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"hour_cos",
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"weekday",
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"is_weekday",
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"load_24h_ago",
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"load_7d_ago",
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"energy_24h_rolling",
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"load_3d_same_time_mean",
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"load_7d_same_time_mean",
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]
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def _history_features(df):
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out = df.copy()
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out["load_24h_ago"] = out["Hausverbrauch"].shift(288)
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out["load_7d_ago"] = out["Hausverbrauch"].shift(2016)
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out["energy_24h_rolling"] = (out["Hausverbrauch"] * 5 / 60 / 1000).shift(1).rolling(288).sum()
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same_time_lags = [out["Hausverbrauch"].shift(288 * d) for d in range(1, 8)]
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out["load_3d_same_time_mean"] = pd.concat(same_time_lags[:3], axis=1).mean(axis=1)
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out["load_7d_same_time_mean"] = pd.concat(same_time_lags, axis=1).mean(axis=1)
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for col, default in [("temp_c", 15.0), ("cloud", 20.0)]:
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if col not in out.columns:
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out[col] = default
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out[col] = pd.to_numeric(out[col], errors="coerce").ffill().bfill().fillna(default)
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return out
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def train(data_obj):
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aid = data_obj["config"]["anlagen_id"]
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df = data_obj.get("df_load_training", data_obj["df_hist"]).copy()
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if "Hausverbrauch" not in df.columns:
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return {"trained": False, "reason": "Hausverbrauch fehlt"}
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df = _history_features(df)
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X = df[FEATURES].astype(float)
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y = pd.to_numeric(df["Hausverbrauch"], errors="coerce")
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valid = X.notna().all(axis=1) & y.notna()
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X, y = X.loc[valid], y.loc[valid]
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if len(X) < 288:
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return {"trained": False, "reason": "zu wenig Daten", "samples": int(len(X))}
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validation_day = X.index.max().normalize()
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validation_mask = X.index >= validation_day
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if int(validation_mask.sum()) < 144:
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validation_day -= datetime.timedelta(days=1)
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validation_mask = (X.index >= validation_day) & (X.index < validation_day + datetime.timedelta(days=1))
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score = None
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if int((~validation_mask).sum()) >= 288 and int(validation_mask.sum()) >= 96:
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eval_model = HistGradientBoostingRegressor(
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max_iter=900,
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max_depth=12,
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learning_rate=0.02,
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min_samples_leaf=8,
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random_state=42,
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)
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eval_model.fit(X.loc[~validation_mask], y.loc[~validation_mask])
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score = float(eval_model.score(X.loc[validation_mask], y.loc[validation_mask]))
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model = HistGradientBoostingRegressor(
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max_iter=1200,
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max_depth=12,
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learning_rate=0.02,
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min_samples_leaf=8,
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random_state=42,
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)
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model.fit(X, y)
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path = save_model(
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aid,
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FORECAST_ID,
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{"model": model, "features": FEATURES, "r2_last_day": score},
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)
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return {
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"trained": True,
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"samples": int(len(X)),
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"r2_last_day": score,
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"path": path,
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}
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def _history_value(history, ts):
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if history is None or history.empty or ts not in history.index or "Hausverbrauch" not in history.columns:
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return None
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value = history.at[ts, "Hausverbrauch"]
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return float(value) if pd.notna(value) and np.isfinite(value) and value >= 0 else None
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def _same_time_values(history, t, days):
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values = []
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for day in range(1, days + 1):
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value = _history_value(history, t - datetime.timedelta(days=day))
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if value is not None and np.isfinite(value):
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values.append(max(0.0, value))
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return values
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def predict(data_obj):
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aid = data_obj["config"]["anlagen_id"]
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artifact = load_model(aid, FORECAST_ID)
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model = artifact["model"] if artifact and "model" in artifact else None
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try:
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score = float(artifact.get("r2_last_day")) if artifact and artifact.get("r2_last_day") is not None else 0.0
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except (TypeError, ValueError):
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score = 0.0
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model_weight = min(0.35, max(0.0, score) * 0.35) if np.isfinite(score) else 0.0
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frames = [
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frame
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for frame in (
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data_obj.get("df_load_training"),
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data_obj.get("df_hist"),
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data_obj.get("df_recent_raw"),
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)
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if frame is not None and not frame.empty and "Hausverbrauch" in frame.columns
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]
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history = pd.concat(frames).sort_index() if frames else pd.DataFrame(columns=["Hausverbrauch"])
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history = history[~history.index.duplicated(keep="last")]
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fut = data_obj["df_fut"]
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res = {}
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for t in fut.index:
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same_values = _same_time_values(history, t, 7)
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load_24 = _history_value(history, t - datetime.timedelta(days=1))
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load_7d = _history_value(history, t - datetime.timedelta(days=7))
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if load_24 is None:
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load_24 = load_7d if load_7d is not None else profile_source_value(history, t, "Hausverbrauch")
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if load_7d is None:
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load_7d = load_24
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same_median = float(np.median(same_values)) if same_values else load_7d
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profile = max(0.0, (0.50 * load_24) + (0.30 * load_7d) + (0.20 * same_median))
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window = history.loc[
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t - datetime.timedelta(days=1):t - datetime.timedelta(minutes=5),
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"Hausverbrauch",
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]
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energy = float((window.sum() * 5 / 60) / 1000.0) if not window.empty else 0.0
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same_3 = same_values[:3]
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row = pd.DataFrame([[
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float(fut.at[t, "temp_c"]),
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float(fut.at[t, "cloud"]),
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float(fut.at[t, "hour_sin"]),
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float(fut.at[t, "hour_cos"]),
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int(fut.at[t, "weekday"]),
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int(fut.at[t, "is_weekday"]),
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load_24,
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load_7d,
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energy,
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float(np.mean(same_3)) if same_3 else profile,
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float(np.mean(same_values)) if same_values else profile,
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]], columns=FEATURES)
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pred = profile
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if model is not None and model_weight > 0.0:
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model_pred = max(0.0, float(model.predict(row)[0]))
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model_pred = min(max(model_pred, profile * 0.25), max(500.0, profile * 3.0))
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pred = ((1.0 - model_weight) * profile) + (model_weight * model_pred)
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res[t] = pred
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history.loc[t, "Hausverbrauch"] = pred
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return res
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