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 @@
+from influxdb_client import Point, WritePrecision
+
+DT_H = 5.0 / 60.0
+
+
+def _f(config, key, default):
+    try:
+        return float(config.get(key, default) or default)
+    except Exception:
+        return float(default)
+
+
+def battery_soc_points(data_obj, forecast_var, pv_dict, load_dict, grid_dict):
+    config = data_obj["config"]
+    aid = str(config["anlagen_id"])
+    cap_kwh = _f(config, "batt_capacity_kwh", 0.0)
+    if cap_kwh <= 0 or not grid_dict:
+        return []
+
+    min_soc = _f(config, "batt_min_soc", _f(config, "batt_min_soc_percent", 0.0))
+    max_soc = _f(config, "batt_max_soc", _f(config, "batt_max_soc_percent", 100.0))
+    charge_eff = max(0.01, min(1.0, _f(config, "batt_charge_efficiency", 0.95)))
+    discharge_eff = max(0.01, min(1.0, _f(config, "batt_discharge_efficiency", 0.95)))
+    start_soc_value = data_obj.get("current_soc_perc")
+    if start_soc_value is None or start_soc_value == "":
+        start_soc_value = config.get("batt_soc_percent", 50.0)
+    start_soc = float(start_soc_value)
+    start_soc = max(min_soc, min(max_soc, start_soc))
+
+    min_kwh = cap_kwh * min_soc / 100.0
+    max_kwh = cap_kwh * max_soc / 100.0
+    soc_kwh = max(min_kwh, min(max_kwh, cap_kwh * start_soc / 100.0))
+
+    planned_battery = (
+        data_obj.get("battery_plans", {})
+        .get(int(forecast_var), {})
+        .get("battery", {})
+    )
+    points = []
+    for t in data_obj["df_fut"].index:
+        if t not in grid_dict:
+            continue
+        if t in planned_battery:
+            battery_target_w = float(planned_battery[t])  # positiv = laden
+        else:
+            residual_w = float(load_dict.get(t, 0.0)) - float(pv_dict.get(t, 0.0))
+            grid_w = float(grid_dict.get(t, 0.0))
+            battery_target_w = grid_w - residual_w
+
+        if battery_target_w > 0:
+            soc_kwh += battery_target_w * DT_H / 1000.0 * charge_eff
+        elif battery_target_w < 0:
+            soc_kwh += battery_target_w * DT_H / 1000.0 / discharge_eff
+
+        soc_kwh = max(min_kwh, min(max_kwh, soc_kwh))
+        soc_percent = max(0.0, min(100.0, soc_kwh / cap_kwh * 100.0))
+
+        points.append(
+            Point("forecast_diagnostics")
+            .tag("anlagen_id", aid)
+            .tag("data_type", "battery_soc_simulation")
+            .tag("forecast_var", f"prog_var_{forecast_var}")
+            .field("soc_percent", float(soc_percent))
+            .time(t.to_pydatetime() if hasattr(t, "to_pydatetime") else t, WritePrecision.S)
+        )
+    return points