dcbb5de98c
- services/planning/forecast.py: compute_correction_factor, apply_forecast_correction - services/planning/db_io.py: _ev_session_from_json, _load_site_context, _load_previous_plan_charge_commitment_prev_w, _load_slots, _build_slot_inputs, _save_planning_run, _save_failed_planning_run - .claude/settings.json: projektový allowlist (autonomní běhy bez promptů) Fasáda beze změny chování; golden 5/5, baseline faily beze změny. planning_engine.py: 6345 → 5717 řádků. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
98 lines
3.1 KiB
Python
98 lines
3.1 KiB
Python
# backend/services/planning/forecast.py
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#
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# EMS plánovač – korekce FVE forecastu podle skutečné výroby
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# (Fáze 1 dekompozice, čistý přesun z planning_engine.py).
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import json
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import logging
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from dataclasses import replace
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from datetime import datetime, timedelta
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from typing import Optional
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from services.planning.constants import (
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CORRECTION_DECAY_SLOTS,
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CORRECTION_MAX_CLAMP,
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CORRECTION_MIN_CLAMP,
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CORRECTION_WINDOW_H,
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)
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from services.planning.types import PlanningSlot, _parse_json_dt
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logger = logging.getLogger(__name__)
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async def compute_correction_factor(
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site_id: int,
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now: datetime,
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db,
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window_h: float = CORRECTION_WINDOW_H,
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) -> tuple[float, dict]:
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"""
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Spočítá korekční faktor FVE forecastu z posledních window_h hodin.
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Vrátí (factor, log_data) kde factor je v rozsahu [CORRECTION_MIN_CLAMP, CORRECTION_MAX_CLAMP].
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factor = 1.0 pokud není dostatek dat nebo je rozdíl zanedbatelný.
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"""
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window_start = now - timedelta(hours=window_h)
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raw = await db.fetchval(
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"""
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select ems.fn_pv_forecast_correction_factor(
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$1::int, $2::timestamptz, $3::timestamptz,
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$4::numeric, $5::numeric
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)
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""",
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site_id,
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window_start,
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now,
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CORRECTION_MIN_CLAMP,
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CORRECTION_MAX_CLAMP,
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)
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j = raw if isinstance(raw, dict) else json.loads(raw)
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factor = float(j.get("correction_factor", 1.0))
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# JSON z DB má často ISO řetězce; asyncpg u $2/$3 vyžaduje datetime
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ws = _parse_json_dt(j.get("window_start")) or window_start
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we = _parse_json_dt(j.get("window_end")) or now
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log_data = {
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"window_start": ws,
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"window_end": we,
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"actual_pv_wh": j.get("actual_pv_wh"),
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"forecast_pv_wh": j.get("forecast_pv_wh"),
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"correction_factor": factor,
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"reason": j.get("reason", "ok"),
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}
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if j.get("raw_factor") is not None:
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log_data["raw_factor"] = j["raw_factor"]
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return factor, log_data
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def apply_forecast_correction(
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slots: list[PlanningSlot],
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now: datetime,
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factor: float,
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decay_slots: int = CORRECTION_DECAY_SLOTS,
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) -> list[PlanningSlot]:
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"""
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Aplikuje korekční faktor na FVE forecast zbývajících slotů.
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Korekce se lineárně utlumuje: na 1. slotu plná korekce,
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na decay_slots-tém slotu žádná korekce.
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Příklad: factor=0.85, slot 0 → pv_a *= 0.85, slot 8 → pv_a *= 0.925, slot 16+ → žádná korekce
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"""
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corrected = []
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for i, slot in enumerate(slots):
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if factor == 1.0 or i >= decay_slots:
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corrected.append(slot)
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continue
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# Lineární útlum: weight klesá od 1.0 (slot 0) do 0.0 (slot decay_slots)
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weight = 1.0 - (i / decay_slots)
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effective_factor = 1.0 + (factor - 1.0) * weight
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corrected.append(
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replace(
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slot,
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pv_a_forecast_w=max(0, int(slot.pv_a_forecast_w * effective_factor)),
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pv_b_forecast_w=max(0, int(slot.pv_b_forecast_w * effective_factor)),
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)
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)
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return corrected
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