mirror of
https://github.com/jpawlowski/hass.tibber_prices.git
synced 2026-03-29 21:03:40 +00:00
9c3c094305
Fixed multiple calculation issues with negative prices (Norway/Germany renewable surplus scenarios): Bug #6: Rating threshold validation with dead code - Added threshold validation (low >= high) with warning - Returns NORMAL as fallback for misconfigured thresholds Bug #7: Min/Max functions returning 0.0 instead of None - Changed default from 0.0 to None when window is empty - Prevents misinterpretation (0.0 looks like price with negatives) Bug #9: Period price diff percentage wrong sign with negative reference - Use abs(ref_price) in percentage calculation - Correct percentage direction for negative prices Bug #10: Trend diff percentage wrong sign with negative current price - Use abs(current_interval_price) in percentage calculation - Correct trend direction when prices cross zero Bug #11: later_half_diff calculation failed for negative prices - Changed condition from `if current_interval_price > 0` to `!= 0` - Use abs(current_interval_price) for percentage Changes: - utils/price.py: Add threshold validation, use abs() in percentages - utils/average.py: Return None instead of 0.0 for empty windows - period_statistics.py: Use abs() for reference prices - trend.py: Use abs() for current prices, fix zero-check condition - tests: 95+ new tests covering negative/zero/mixed price scenarios Impact: All calculations work correctly with negative electricity prices. Percentages show correct direction regardless of sign.
501 lines
16 KiB
Python
501 lines
16 KiB
Python
"""Utility functions for calculating price averages."""
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from __future__ import annotations
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from datetime import datetime, timedelta
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from custom_components.tibber_prices.coordinator.time_service import TibberPricesTimeService
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def calculate_trailing_24h_avg(all_prices: list[dict], interval_start: datetime) -> float | None:
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"""
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Calculate trailing 24-hour average price for a given interval.
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Args:
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all_prices: List of all price data (yesterday, today, tomorrow combined)
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interval_start: Start time of the interval to calculate average for
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time: TibberPricesTimeService instance (required)
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Returns:
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Average price for the 24 hours preceding the interval, or None if no data in window
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"""
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# Define the 24-hour window: from 24 hours before interval_start up to interval_start
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window_start = interval_start - timedelta(hours=24)
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window_end = interval_start
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# Filter prices within the 24-hour window
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prices_in_window = []
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for price_data in all_prices:
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starts_at = price_data["startsAt"] # Already datetime object in local timezone
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if starts_at is None:
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continue
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# Include intervals that start within the window (not including the current interval's end)
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if window_start <= starts_at < window_end:
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prices_in_window.append(float(price_data["total"]))
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# Calculate average
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# CRITICAL: Return None instead of 0.0 when no data available
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# With negative prices, 0.0 could be misinterpreted as a real average value
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if prices_in_window:
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return sum(prices_in_window) / len(prices_in_window)
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return None
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def calculate_leading_24h_avg(all_prices: list[dict], interval_start: datetime) -> float | None:
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"""
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Calculate leading 24-hour average price for a given interval.
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Args:
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all_prices: List of all price data (yesterday, today, tomorrow combined)
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interval_start: Start time of the interval to calculate average for
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time: TibberPricesTimeService instance (required)
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Returns:
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Average price for up to 24 hours following the interval, or None if no data in window
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"""
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# Define the 24-hour window: from interval_start up to 24 hours after
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window_start = interval_start
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window_end = interval_start + timedelta(hours=24)
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# Filter prices within the 24-hour window
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prices_in_window = []
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for price_data in all_prices:
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starts_at = price_data["startsAt"] # Already datetime object in local timezone
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if starts_at is None:
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continue
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# Include intervals that start within the window
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if window_start <= starts_at < window_end:
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prices_in_window.append(float(price_data["total"]))
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# Calculate average
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# CRITICAL: Return None instead of 0.0 when no data available
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# With negative prices, 0.0 could be misinterpreted as a real average value
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if prices_in_window:
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return sum(prices_in_window) / len(prices_in_window)
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return None
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def calculate_current_trailing_avg(
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coordinator_data: dict,
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*,
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time: TibberPricesTimeService,
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) -> float | None:
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"""
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Calculate the trailing 24-hour average for the current time.
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Args:
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coordinator_data: The coordinator data containing priceInfo
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time: TibberPricesTimeService instance (required)
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Returns:
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Current trailing 24-hour average price, or None if unavailable
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"""
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if not coordinator_data:
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return None
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price_info = coordinator_data.get("priceInfo", {})
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yesterday_prices = price_info.get("yesterday", [])
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today_prices = price_info.get("today", [])
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tomorrow_prices = price_info.get("tomorrow", [])
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all_prices = yesterday_prices + today_prices + tomorrow_prices
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if not all_prices:
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return None
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now = time.now()
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return calculate_trailing_24h_avg(all_prices, now)
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def calculate_current_leading_avg(
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coordinator_data: dict,
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*,
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time: TibberPricesTimeService,
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) -> float | None:
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"""
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Calculate the leading 24-hour average for the current time.
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Args:
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coordinator_data: The coordinator data containing priceInfo
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time: TibberPricesTimeService instance (required)
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Returns:
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Current leading 24-hour average price, or None if unavailable
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"""
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if not coordinator_data:
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return None
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price_info = coordinator_data.get("priceInfo", {})
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yesterday_prices = price_info.get("yesterday", [])
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today_prices = price_info.get("today", [])
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tomorrow_prices = price_info.get("tomorrow", [])
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all_prices = yesterday_prices + today_prices + tomorrow_prices
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if not all_prices:
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return None
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now = time.now()
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return calculate_leading_24h_avg(all_prices, now)
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def calculate_trailing_24h_min(
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all_prices: list[dict],
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interval_start: datetime,
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*,
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time: TibberPricesTimeService,
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) -> float | None:
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"""
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Calculate trailing 24-hour minimum price for a given interval.
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Args:
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all_prices: List of all price data (yesterday, today, tomorrow combined)
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interval_start: Start time of the interval to calculate minimum for
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time: TibberPricesTimeService instance (required)
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Returns:
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Minimum price for the 24 hours preceding the interval, or None if no data in window
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"""
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# Define the 24-hour window: from 24 hours before interval_start up to interval_start
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window_start = interval_start - timedelta(hours=24)
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window_end = interval_start
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# Filter prices within the 24-hour window
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prices_in_window = []
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for price_data in all_prices:
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starts_at = time.get_interval_time(price_data)
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if starts_at is None:
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continue
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# Include intervals that start within the window (not including the current interval's end)
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if window_start <= starts_at < window_end:
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prices_in_window.append(float(price_data["total"]))
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# Calculate minimum
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# CRITICAL: Return None instead of 0.0 when no data available
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# With negative prices, 0.0 could be misinterpreted as a maximum value
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if prices_in_window:
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return min(prices_in_window)
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return None
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def calculate_trailing_24h_max(
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all_prices: list[dict],
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interval_start: datetime,
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*,
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time: TibberPricesTimeService,
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) -> float | None:
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"""
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Calculate trailing 24-hour maximum price for a given interval.
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Args:
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all_prices: List of all price data (yesterday, today, tomorrow combined)
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interval_start: Start time of the interval to calculate maximum for
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time: TibberPricesTimeService instance (required)
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Returns:
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Maximum price for the 24 hours preceding the interval, or None if no data in window
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"""
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# Define the 24-hour window: from 24 hours before interval_start up to interval_start
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window_start = interval_start - timedelta(hours=24)
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window_end = interval_start
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# Filter prices within the 24-hour window
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prices_in_window = []
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for price_data in all_prices:
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starts_at = time.get_interval_time(price_data)
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if starts_at is None:
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continue
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# Include intervals that start within the window (not including the current interval's end)
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if window_start <= starts_at < window_end:
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prices_in_window.append(float(price_data["total"]))
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# Calculate maximum
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# CRITICAL: Return None instead of 0.0 when no data available
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# With negative prices, 0.0 could be misinterpreted as a real price value
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if prices_in_window:
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return max(prices_in_window)
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return None
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def calculate_leading_24h_min(
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all_prices: list[dict],
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interval_start: datetime,
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*,
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time: TibberPricesTimeService,
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) -> float | None:
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"""
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Calculate leading 24-hour minimum price for a given interval.
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Args:
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all_prices: List of all price data (yesterday, today, tomorrow combined)
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interval_start: Start time of the interval to calculate minimum for
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time: TibberPricesTimeService instance (required)
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Returns:
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Minimum price for up to 24 hours following the interval, or None if no data in window
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"""
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# Define the 24-hour window: from interval_start up to 24 hours after
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window_start = interval_start
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window_end = interval_start + timedelta(hours=24)
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# Filter prices within the 24-hour window
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prices_in_window = []
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for price_data in all_prices:
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starts_at = time.get_interval_time(price_data)
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if starts_at is None:
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continue
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# Include intervals that start within the window
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if window_start <= starts_at < window_end:
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prices_in_window.append(float(price_data["total"]))
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# Calculate minimum
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# CRITICAL: Return None instead of 0.0 when no data available
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# With negative prices, 0.0 could be misinterpreted as a maximum value
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if prices_in_window:
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return min(prices_in_window)
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return None
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def calculate_leading_24h_max(
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all_prices: list[dict],
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interval_start: datetime,
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*,
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time: TibberPricesTimeService,
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) -> float | None:
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"""
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Calculate leading 24-hour maximum price for a given interval.
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Args:
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all_prices: List of all price data (yesterday, today, tomorrow combined)
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interval_start: Start time of the interval to calculate maximum for
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time: TibberPricesTimeService instance (required)
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Returns:
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Maximum price for up to 24 hours following the interval, or None if no data in window
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"""
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# Define the 24-hour window: from interval_start up to 24 hours after
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window_start = interval_start
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window_end = interval_start + timedelta(hours=24)
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# Filter prices within the 24-hour window
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prices_in_window = []
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for price_data in all_prices:
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starts_at = time.get_interval_time(price_data)
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if starts_at is None:
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continue
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# Include intervals that start within the window
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if window_start <= starts_at < window_end:
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prices_in_window.append(float(price_data["total"]))
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# Calculate maximum
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# CRITICAL: Return None instead of 0.0 when no data available
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# With negative prices, 0.0 could be misinterpreted as a real price value
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if prices_in_window:
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return max(prices_in_window)
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return None
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def calculate_current_trailing_min(
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coordinator_data: dict,
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*,
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time: TibberPricesTimeService,
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) -> float | None:
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"""
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Calculate the trailing 24-hour minimum for the current time.
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Args:
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coordinator_data: The coordinator data containing priceInfo
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time: TibberPricesTimeService instance (required)
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Returns:
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Current trailing 24-hour minimum price, or None if unavailable
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"""
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if not coordinator_data:
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return None
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price_info = coordinator_data.get("priceInfo", {})
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yesterday_prices = price_info.get("yesterday", [])
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today_prices = price_info.get("today", [])
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tomorrow_prices = price_info.get("tomorrow", [])
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all_prices = yesterday_prices + today_prices + tomorrow_prices
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if not all_prices:
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return None
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now = time.now()
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return calculate_trailing_24h_min(all_prices, now, time=time)
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def calculate_current_trailing_max(
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coordinator_data: dict,
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*,
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time: TibberPricesTimeService,
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) -> float | None:
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"""
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Calculate the trailing 24-hour maximum for the current time.
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Args:
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coordinator_data: The coordinator data containing priceInfo
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time: TibberPricesTimeService instance (required)
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Returns:
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Current trailing 24-hour maximum price, or None if unavailable
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"""
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if not coordinator_data:
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return None
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price_info = coordinator_data.get("priceInfo", {})
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yesterday_prices = price_info.get("yesterday", [])
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today_prices = price_info.get("today", [])
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tomorrow_prices = price_info.get("tomorrow", [])
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all_prices = yesterday_prices + today_prices + tomorrow_prices
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if not all_prices:
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return None
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now = time.now()
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return calculate_trailing_24h_max(all_prices, now, time=time)
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def calculate_current_leading_min(
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coordinator_data: dict,
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*,
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time: TibberPricesTimeService,
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) -> float | None:
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"""
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Calculate the leading 24-hour minimum for the current time.
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Args:
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coordinator_data: The coordinator data containing priceInfo
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time: TibberPricesTimeService instance (required)
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Returns:
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Current leading 24-hour minimum price, or None if unavailable
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"""
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if not coordinator_data:
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return None
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price_info = coordinator_data.get("priceInfo", {})
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yesterday_prices = price_info.get("yesterday", [])
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today_prices = price_info.get("today", [])
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tomorrow_prices = price_info.get("tomorrow", [])
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all_prices = yesterday_prices + today_prices + tomorrow_prices
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if not all_prices:
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return None
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now = time.now()
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return calculate_leading_24h_min(all_prices, now, time=time)
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def calculate_current_leading_max(
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coordinator_data: dict,
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*,
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time: TibberPricesTimeService,
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) -> float | None:
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"""
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Calculate the leading 24-hour maximum for the current time.
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Args:
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coordinator_data: The coordinator data containing priceInfo
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time: TibberPricesTimeService instance (required)
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Returns:
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Current leading 24-hour maximum price, or None if unavailable
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"""
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if not coordinator_data:
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return None
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price_info = coordinator_data.get("priceInfo", {})
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yesterday_prices = price_info.get("yesterday", [])
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today_prices = price_info.get("today", [])
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tomorrow_prices = price_info.get("tomorrow", [])
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all_prices = yesterday_prices + today_prices + tomorrow_prices
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if not all_prices:
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return None
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now = time.now()
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return calculate_leading_24h_max(all_prices, now, time=time)
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def calculate_next_n_hours_avg(
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coordinator_data: dict,
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hours: int,
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*,
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time: TibberPricesTimeService,
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) -> float | None:
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"""
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Calculate average price for the next N hours starting from the next interval.
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This function computes the average of all 15-minute intervals starting from
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the next interval (not current) up to N hours into the future.
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Args:
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coordinator_data: The coordinator data containing priceInfo
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hours: Number of hours to look ahead (1, 2, 3, 4, 5, 6, 8, 12, etc.)
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time: TibberPricesTimeService instance (required)
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Returns:
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Average price for the next N hours, or None if insufficient data
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"""
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if not coordinator_data or hours <= 0:
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return None
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price_info = coordinator_data.get("priceInfo", {})
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yesterday_prices = price_info.get("yesterday", [])
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today_prices = price_info.get("today", [])
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tomorrow_prices = price_info.get("tomorrow", [])
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all_prices = yesterday_prices + today_prices + tomorrow_prices
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if not all_prices:
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return None
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# Find the current interval index
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current_idx = None
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for idx, price_data in enumerate(all_prices):
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starts_at = time.get_interval_time(price_data)
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if starts_at is None:
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continue
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interval_end = starts_at + time.get_interval_duration()
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if time.is_current_interval(starts_at, interval_end):
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current_idx = idx
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break
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if current_idx is None:
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return None
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# Calculate how many intervals are in N hours
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intervals_needed = time.minutes_to_intervals(hours * 60)
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# Collect prices starting from NEXT interval (current_idx + 1)
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prices_in_window = []
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for offset in range(1, intervals_needed + 1):
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idx = current_idx + offset
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if idx >= len(all_prices):
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# Not enough future data available
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break
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price = all_prices[idx].get("total")
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if price is not None:
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prices_in_window.append(float(price))
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# Return None if no data at all
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if not prices_in_window:
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return None
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# Return average (prefer full period, but allow graceful degradation)
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return sum(prices_in_window) / len(prices_in_window)
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