Merge commit 'd8e4449f1009bc03b167c0e5667413585b2b3e53' as 'core'
This commit is contained in:
Du Wenbo
606
core/backend/app/services/ai_prediction.py
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606
core/backend/app/services/ai_prediction.py
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"""AI预测引擎 - 光伏发电、负荷、热泵COP预测与自发自用优化
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Uses physics-based models from weather_model.py combined with statistical
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methods (moving averages, exponential smoothing, seasonal decomposition)
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to generate realistic forecasts. Inspired by Envision's 天枢能源大模型.
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"""
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import math
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import logging
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from datetime import datetime, timezone, timedelta
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from typing import Optional
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import numpy as np
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from sqlalchemy import select, func, and_
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.models.device import Device
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from app.models.energy import EnergyData
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from app.models.prediction import PredictionTask, PredictionResult, OptimizationSchedule
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from app.services.weather_model import (
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outdoor_temperature, solar_altitude, get_cloud_factor,
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pv_power as _physics_pv_power, get_pv_orientation,
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get_hvac_mode, MONTHLY_AVG_TEMP, MONTHLY_DIURNAL_SWING,
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)
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logger = logging.getLogger("ai_prediction")
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# Beijing electricity TOU pricing (yuan/kWh) - simplified
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TOU_PRICE = {
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"peak": 1.2, # 10:00-15:00, 18:00-21:00
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"shoulder": 0.8, # 07:00-10:00, 15:00-18:00, 21:00-23:00
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"valley": 0.4, # 23:00-07:00
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}
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def _get_tou_price(hour: int) -> float:
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if 10 <= hour < 15 or 18 <= hour < 21:
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return TOU_PRICE["peak"]
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elif 7 <= hour < 10 or 15 <= hour < 18 or 21 <= hour < 23:
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return TOU_PRICE["shoulder"]
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else:
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return TOU_PRICE["valley"]
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# ---------------------------------------------------------------------------
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# PV Power Forecasting
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# ---------------------------------------------------------------------------
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async def forecast_pv(
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db: AsyncSession,
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device_id: int,
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horizon_hours: int = 24,
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) -> list[dict]:
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"""Forecast PV generation for the next horizon_hours.
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Combines physics-based solar model with historical pattern correction.
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Returns list of {timestamp, predicted_power_kw, confidence_lower, confidence_upper}.
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"""
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device = await db.get(Device, device_id)
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if not device:
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raise ValueError(f"Device {device_id} not found")
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rated_power = device.rated_power or 110.0
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orientation = get_pv_orientation(device.code or "")
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# Fetch recent historical data for pattern correction
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now = datetime.now(timezone.utc)
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lookback = now - timedelta(days=7)
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result = await db.execute(
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select(EnergyData.timestamp, EnergyData.value)
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.where(and_(
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EnergyData.device_id == device_id,
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EnergyData.data_type == "power",
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EnergyData.timestamp >= lookback,
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))
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.order_by(EnergyData.timestamp)
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)
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historical = result.all()
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# Calculate hourly averages from history for bias correction
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hourly_actual: dict[int, list[float]] = {h: [] for h in range(24)}
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for ts, val in historical:
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beijing_h = (ts.hour + 8) % 24 if ts.tzinfo else ts.hour
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hourly_actual[beijing_h].append(val)
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hourly_avg = {
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h: np.mean(vals) if vals else None
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for h, vals in hourly_actual.items()
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}
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# Generate physics-based forecast with bias correction
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forecasts = []
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for h_offset in range(horizon_hours):
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target_utc = now + timedelta(hours=h_offset)
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target_utc = target_utc.replace(minute=0, second=0, microsecond=0)
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# Physics model baseline
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base_power = _physics_pv_power(target_utc, rated_power=rated_power,
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orientation=orientation,
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device_code=device.code or "")
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# Bias correction from recent history
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beijing_hour = (target_utc.hour + 8) % 24
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hist_avg = hourly_avg.get(beijing_hour)
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if hist_avg is not None and base_power > 0:
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# Blend: 70% physics + 30% historical pattern
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correction = hist_avg / max(base_power, 0.1)
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correction = max(0.7, min(1.3, correction))
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predicted = base_power * (0.7 + 0.3 * correction)
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else:
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predicted = base_power
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# Confidence interval widens with forecast horizon
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uncertainty = 0.05 + 0.02 * h_offset # grows with time
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uncertainty = min(uncertainty, 0.40)
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margin = predicted * uncertainty
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conf_lower = max(0, predicted - margin)
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conf_upper = min(rated_power, predicted + margin)
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forecasts.append({
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"timestamp": target_utc.isoformat(),
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"predicted_power_kw": round(predicted, 2),
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"confidence_lower": round(conf_lower, 2),
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"confidence_upper": round(conf_upper, 2),
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})
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return forecasts
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# ---------------------------------------------------------------------------
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# Load Forecasting
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# ---------------------------------------------------------------------------
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async def forecast_load(
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db: AsyncSession,
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device_id: Optional[int] = None,
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building_type: str = "office",
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horizon_hours: int = 24,
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) -> list[dict]:
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"""Forecast building electricity load.
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Uses day-of-week patterns, hourly profiles, and seasonal temperature
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correlation. If device_id is None, forecasts aggregate campus load.
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"""
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now = datetime.now(timezone.utc)
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beijing_now = now + timedelta(hours=8)
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# Fetch recent history for pattern calibration
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lookback = now - timedelta(days=14)
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conditions = [
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EnergyData.data_type == "power",
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EnergyData.timestamp >= lookback,
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]
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if device_id:
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conditions.append(EnergyData.device_id == device_id)
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else:
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# Aggregate all meters
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conditions.append(
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EnergyData.device_id.in_(
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select(Device.id).where(Device.device_type == "meter")
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)
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)
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result = await db.execute(
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select(EnergyData.timestamp, EnergyData.value)
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.where(and_(*conditions))
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.order_by(EnergyData.timestamp)
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)
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historical = result.all()
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# Build weekday/weekend hourly profiles from history
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weekday_profile: dict[int, list[float]] = {h: [] for h in range(24)}
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weekend_profile: dict[int, list[float]] = {h: [] for h in range(24)}
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for ts, val in historical:
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bj = ts + timedelta(hours=8) if ts.tzinfo else ts
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h = bj.hour
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if bj.weekday() >= 5:
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weekend_profile[h].append(val)
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else:
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weekday_profile[h].append(val)
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# Default load profile if no history
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default_weekday = {
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0: 18, 1: 16, 2: 16, 3: 15, 4: 15, 5: 17, 6: 25, 7: 40,
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8: 55, 9: 60, 10: 62, 11: 58, 12: 45, 13: 58, 14: 62,
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15: 60, 16: 55, 17: 48, 18: 35, 19: 28, 20: 25, 21: 22, 22: 20, 23: 18,
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}
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default_weekend = {h: v * 0.5 for h, v in default_weekday.items()}
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def _avg_or_default(profile, defaults, h):
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vals = profile.get(h, [])
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return float(np.mean(vals)) if vals else defaults[h]
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forecasts = []
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for h_offset in range(horizon_hours):
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target_utc = now + timedelta(hours=h_offset)
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target_utc = target_utc.replace(minute=0, second=0, microsecond=0)
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bj = target_utc + timedelta(hours=8)
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hour = bj.hour
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is_weekend = bj.weekday() >= 5
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if is_weekend:
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base_load = _avg_or_default(weekend_profile, default_weekend, hour)
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else:
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base_load = _avg_or_default(weekday_profile, default_weekday, hour)
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# Temperature correction: HVAC adds load in extreme temps
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temp = outdoor_temperature(target_utc)
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if temp < 5:
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hvac_factor = 1.0 + 0.02 * (5 - temp)
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elif temp > 28:
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hvac_factor = 1.0 + 0.025 * (temp - 28)
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else:
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hvac_factor = 1.0
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hvac_factor = min(hvac_factor, 1.4)
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predicted = base_load * hvac_factor
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# Factory buildings have flatter profiles
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if building_type == "factory":
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predicted = predicted * 0.85 + base_load * 0.15
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# Confidence interval
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uncertainty = 0.08 + 0.015 * h_offset
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uncertainty = min(uncertainty, 0.35)
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margin = predicted * uncertainty
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forecasts.append({
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"timestamp": target_utc.isoformat(),
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"predicted_load_kw": round(predicted, 2),
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"confidence_lower": round(max(0, predicted - margin), 2),
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"confidence_upper": round(predicted + margin, 2),
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})
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return forecasts
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# ---------------------------------------------------------------------------
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# Heat Pump COP Prediction
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# ---------------------------------------------------------------------------
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async def forecast_heatpump_cop(
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db: AsyncSession,
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device_id: int,
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horizon_hours: int = 24,
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) -> list[dict]:
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"""Predict heat pump COP based on outdoor temperature forecast.
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COP model: COP = base_cop + 0.05 * (T_outdoor - 7), clamped [2.0, 5.5].
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Returns optimal operating windows ranked by expected COP.
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"""
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device = await db.get(Device, device_id)
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if not device:
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raise ValueError(f"Device {device_id} not found")
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now = datetime.now(timezone.utc)
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forecasts = []
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for h_offset in range(horizon_hours):
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target_utc = now + timedelta(hours=h_offset)
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target_utc = target_utc.replace(minute=0, second=0, microsecond=0)
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bj = target_utc + timedelta(hours=8)
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temp = outdoor_temperature(target_utc)
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mode = get_hvac_mode(bj.month)
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# COP model (same as weather_model but deterministic for forecast)
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if mode in ("heating", "transition_spring", "transition_fall"):
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cop = 3.0 + 0.05 * (temp - 7)
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else: # cooling
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cop = 4.0 - 0.04 * (temp - 25)
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cop = max(2.0, min(5.5, cop))
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# Estimated power at this COP
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rated = device.rated_power or 35.0
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# Load factor based on time and mode
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if mode == "heating":
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if 6 <= bj.hour < 9:
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load_factor = 0.85
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elif 9 <= bj.hour < 16:
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load_factor = 0.55
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elif 16 <= bj.hour < 22:
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load_factor = 0.75
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else:
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load_factor = 0.65
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elif mode == "cooling":
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if 11 <= bj.hour < 16:
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load_factor = 0.85
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elif 8 <= bj.hour < 11 or 16 <= bj.hour < 19:
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load_factor = 0.60
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else:
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load_factor = 0.25
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else:
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load_factor = 0.35
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if bj.weekday() >= 5:
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load_factor *= 0.7
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est_power = rated * load_factor
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electricity_price = _get_tou_price(bj.hour)
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operating_cost = est_power * electricity_price # yuan/h
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forecasts.append({
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"timestamp": target_utc.isoformat(),
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"predicted_cop": round(cop, 2),
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"outdoor_temp": round(temp, 1),
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"estimated_power_kw": round(est_power, 2),
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"load_factor": round(load_factor, 2),
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"electricity_price": electricity_price,
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"operating_cost_yuan_h": round(operating_cost, 2),
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"mode": mode,
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})
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return forecasts
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# ---------------------------------------------------------------------------
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# Self-Consumption Optimization
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# ---------------------------------------------------------------------------
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async def optimize_self_consumption(
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db: AsyncSession,
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horizon_hours: int = 24,
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) -> dict:
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"""Compare predicted PV generation vs predicted load to find optimization
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opportunities. Recommends heat pump pre-heating during PV surplus.
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Returns:
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- hourly comparison (pv vs load)
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- surplus/deficit periods
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- recommended heat pump schedule
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- expected savings
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"""
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now = datetime.now(timezone.utc)
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# Get all PV inverter device ids
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pv_result = await db.execute(
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select(Device).where(Device.device_type == "pv_inverter", Device.is_active == True)
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)
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pv_devices = pv_result.scalars().all()
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# Aggregate PV forecast
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pv_total = [0.0] * horizon_hours
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for dev in pv_devices:
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pv_forecast = await forecast_pv(db, dev.id, horizon_hours)
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for i, f in enumerate(pv_forecast):
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pv_total[i] += f["predicted_power_kw"]
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# Aggregate load forecast
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load_forecast = await forecast_load(db, device_id=None, horizon_hours=horizon_hours)
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# Build hourly comparison
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hourly = []
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surplus_periods = []
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deficit_periods = []
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total_surplus_kwh = 0.0
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total_deficit_kwh = 0.0
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for i in range(horizon_hours):
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target_utc = now + timedelta(hours=i)
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target_utc = target_utc.replace(minute=0, second=0, microsecond=0)
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bj = target_utc + timedelta(hours=8)
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pv_kw = pv_total[i]
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load_kw = load_forecast[i]["predicted_load_kw"]
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balance = pv_kw - load_kw
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price = _get_tou_price(bj.hour)
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entry = {
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"timestamp": target_utc.isoformat(),
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"hour": bj.hour,
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"pv_generation_kw": round(pv_kw, 2),
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"load_kw": round(load_kw, 2),
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"balance_kw": round(balance, 2),
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"electricity_price": price,
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}
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hourly.append(entry)
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if balance > 2: # >2kW surplus threshold
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surplus_periods.append({"hour": bj.hour, "surplus_kw": round(balance, 2)})
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total_surplus_kwh += balance
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elif balance < -2:
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deficit_periods.append({"hour": bj.hour, "deficit_kw": round(-balance, 2)})
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total_deficit_kwh += (-balance)
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# Generate heat pump optimization schedule
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# Strategy: shift heat pump load to PV surplus periods
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hp_schedule = []
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savings_kwh = 0.0
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savings_yuan = 0.0
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for period in surplus_periods:
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hour = period["hour"]
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surplus = period["surplus_kw"]
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price = _get_tou_price(hour)
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# Use surplus to pre-heat/pre-cool
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usable_power = min(surplus, 35.0) # cap at single HP rated power
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hp_schedule.append({
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"hour": hour,
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"action": "boost",
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"power_kw": round(usable_power, 2),
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"reason": "利用光伏余电预加热/预冷",
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})
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savings_kwh += usable_power
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savings_yuan += usable_power * price
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# Also recommend reducing HP during peak-price deficit periods
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for period in deficit_periods:
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hour = period["hour"]
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price = _get_tou_price(hour)
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if price >= TOU_PRICE["peak"]:
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hp_schedule.append({
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"hour": hour,
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"action": "reduce",
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"power_kw": 0,
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"reason": "高电价时段降低热泵负荷",
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})
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# Estimate savings from reduced operation during peak
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savings_yuan += 5.0 * price # assume 5kW reduction
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self_consumption_rate = 0.0
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total_pv = sum(pv_total)
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total_load = sum(f["predicted_load_kw"] for f in load_forecast)
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if total_pv > 0:
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self_consumed = min(total_pv, total_load)
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self_consumption_rate = self_consumed / total_pv * 100
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return {
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"hourly": hourly,
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"surplus_periods": surplus_periods,
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"deficit_periods": deficit_periods,
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"hp_schedule": hp_schedule,
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"summary": {
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"total_pv_kwh": round(total_pv, 2),
|
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"total_load_kwh": round(total_load, 2),
|
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"total_surplus_kwh": round(total_surplus_kwh, 2),
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"total_deficit_kwh": round(total_deficit_kwh, 2),
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"self_consumption_rate": round(self_consumption_rate, 1),
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"potential_savings_kwh": round(savings_kwh, 2),
|
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"potential_savings_yuan": round(savings_yuan, 2),
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},
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||||
}
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||||
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||||
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||||
# ---------------------------------------------------------------------------
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||||
# Prediction Accuracy
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
async def get_prediction_accuracy(
|
||||
db: AsyncSession,
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||||
prediction_type: Optional[str] = None,
|
||||
days: int = 7,
|
||||
) -> dict:
|
||||
"""Calculate prediction accuracy metrics (MAE, RMSE, MAPE) from
|
||||
historical predictions that have actual values filled in."""
|
||||
cutoff = datetime.now(timezone.utc) - timedelta(days=days)
|
||||
|
||||
conditions = [
|
||||
PredictionResult.actual_value.isnot(None),
|
||||
PredictionResult.timestamp >= cutoff,
|
||||
]
|
||||
if prediction_type:
|
||||
conditions.append(
|
||||
PredictionResult.task_id.in_(
|
||||
select(PredictionTask.id).where(
|
||||
PredictionTask.prediction_type == prediction_type
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
result = await db.execute(
|
||||
select(PredictionResult.predicted_value, PredictionResult.actual_value)
|
||||
.where(and_(*conditions))
|
||||
)
|
||||
pairs = result.all()
|
||||
|
||||
if not pairs:
|
||||
# Return mock accuracy for demo (simulating a well-tuned model)
|
||||
return {
|
||||
"sample_count": 0,
|
||||
"mae": 2.5,
|
||||
"rmse": 3.8,
|
||||
"mape": 8.5,
|
||||
"note": "使用模拟精度指标（无历史预测数据）",
|
||||
}
|
||||
|
||||
predicted = np.array([p[0] for p in pairs])
|
||||
actual = np.array([p[1] for p in pairs])
|
||||
|
||||
errors = predicted - actual
|
||||
mae = float(np.mean(np.abs(errors)))
|
||||
rmse = float(np.sqrt(np.mean(errors ** 2)))
|
||||
|
||||
# MAPE: only where actual > 0 to avoid division by zero
|
||||
mask = actual > 0.1
|
||||
if mask.any():
|
||||
mape = float(np.mean(np.abs(errors[mask] / actual[mask])) * 100)
|
||||
else:
|
||||
mape = 0.0
|
||||
|
||||
return {
|
||||
"sample_count": len(pairs),
|
||||
"mae": round(mae, 2),
|
||||
"rmse": round(rmse, 2),
|
||||
"mape": round(mape, 1),
|
||||
}
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Run Prediction (creates task + results)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
async def run_prediction(
|
||||
db: AsyncSession,
|
||||
device_id: Optional[int],
|
||||
prediction_type: str,
|
||||
horizon_hours: int = 24,
|
||||
parameters: Optional[dict] = None,
|
||||
) -> PredictionTask:
|
||||
"""Execute a prediction and store results in the database."""
|
||||
task = PredictionTask(
|
||||
device_id=device_id,
|
||||
prediction_type=prediction_type,
|
||||
horizon_hours=horizon_hours,
|
||||
status="running",
|
||||
parameters=parameters or {},
|
||||
)
|
||||
db.add(task)
|
||||
await db.flush()
|
||||
|
||||
try:
|
||||
if prediction_type == "pv":
|
||||
if not device_id:
|
||||
raise ValueError("device_id required for PV forecast")
|
||||
forecasts = await forecast_pv(db, device_id, horizon_hours)
|
||||
for f in forecasts:
|
||||
db.add(PredictionResult(
|
||||
task_id=task.id,
|
||||
timestamp=f["timestamp"],
|
||||
predicted_value=f["predicted_power_kw"],
|
||||
confidence_lower=f["confidence_lower"],
|
||||
confidence_upper=f["confidence_upper"],
|
||||
unit="kW",
|
||||
))
|
||||
|
||||
elif prediction_type == "load":
|
||||
building_type = (parameters or {}).get("building_type", "office")
|
||||
forecasts = await forecast_load(db, device_id, building_type, horizon_hours)
|
||||
for f in forecasts:
|
||||
db.add(PredictionResult(
|
||||
task_id=task.id,
|
||||
timestamp=f["timestamp"],
|
||||
predicted_value=f["predicted_load_kw"],
|
||||
confidence_lower=f["confidence_lower"],
|
||||
confidence_upper=f["confidence_upper"],
|
||||
unit="kW",
|
||||
))
|
||||
|
||||
elif prediction_type == "heatpump":
|
||||
if not device_id:
|
||||
raise ValueError("device_id required for heat pump forecast")
|
||||
forecasts = await forecast_heatpump_cop(db, device_id, horizon_hours)
|
||||
for f in forecasts:
|
||||
db.add(PredictionResult(
|
||||
task_id=task.id,
|
||||
timestamp=f["timestamp"],
|
||||
predicted_value=f["predicted_cop"],
|
||||
confidence_lower=max(2.0, f["predicted_cop"] - 0.3),
|
||||
confidence_upper=min(5.5, f["predicted_cop"] + 0.3),
|
||||
unit="",
|
||||
))
|
||||
|
||||
elif prediction_type == "optimization":
|
||||
opt = await optimize_self_consumption(db, horizon_hours)
|
||||
# Store as optimization schedule
|
||||
now = datetime.now(timezone.utc)
|
||||
schedule = OptimizationSchedule(
|
||||
date=now.replace(hour=0, minute=0, second=0, microsecond=0),
|
||||
schedule_data=opt,
|
||||
expected_savings_kwh=opt["summary"]["potential_savings_kwh"],
|
||||
expected_savings_yuan=opt["summary"]["potential_savings_yuan"],
|
||||
status="pending",
|
||||
)
|
||||
db.add(schedule)
|
||||
# Also store hourly balance as prediction results
|
||||
for entry in opt["hourly"]:
|
||||
db.add(PredictionResult(
|
||||
task_id=task.id,
|
||||
timestamp=entry["timestamp"],
|
||||
predicted_value=entry["balance_kw"],
|
||||
unit="kW",
|
||||
))
|
||||
else:
|
||||
raise ValueError(f"Unknown prediction type: {prediction_type}")
|
||||
|
||||
task.status = "completed"
|
||||
task.completed_at = datetime.now(timezone.utc)
|
||||
|
||||
except Exception as e:
|
||||
task.status = "failed"
|
||||
task.error_message = str(e)
|
||||
logger.error(f"Prediction task {task.id} failed: {e}", exc_info=True)
|
||||
|
||||
return task
|
||||
Reference in New Issue
Block a user