jpawlowski/hass.tibber_prices
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feat: Implement time-sensitive updates for Tibber price sensors and binary sensors

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This commit is contained in:
Julian Pawlowski 2025-11-07 21:02:11 +00:00
parent 1ed2c08f34
commit ca88f136c3
3 changed files with 291 additions and 15 deletions
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177
custom_components/tibber_prices/binary_sensor.py
View file

@ -3,7 +3,7 @@
from __future__ import annotations
from datetime import datetime, timedelta
from typing import TYPE_CHECKING
from typing import TYPE_CHECKING, Any
from homeassistant.components.binary_sensor import (
BinarySensorDeviceClass,
@ -11,9 +11,11 @@ from homeassistant.components.binary_sensor import (
BinarySensorEntityDescription,
)
from homeassistant.const import PERCENTAGE, EntityCategory
from homeassistant.core import callback
from homeassistant.util import dt as dt_util
from .average_utils import calculate_leading_24h_avg, calculate_trailing_24h_avg
from .coordinator import TIME_SENSITIVE_ENTITY_KEYS
from .entity import TibberPricesEntity
from .sensor import find_price_data_for_interval
@ -107,6 +109,39 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
self._attr_unique_id = f"{coordinator.config_entry.entry_id}_{entity_description.key}"
self._state_getter: Callable | None = self._get_state_getter()
self._attribute_getter: Callable | None = self._get_attribute_getter()
self._time_sensitive_remove_listener: Callable | None = None
# Cache for expensive period calculations to avoid recalculating twice
# (once for is_on, once for attributes)
self._period_cache: dict[str, Any] = {}
self._cache_key: str = ""
async def async_added_to_hass(self) -> None:
"""When entity is added to hass."""
await super().async_added_to_hass()
# Register with coordinator for time-sensitive updates if applicable
if self.entity_description.key in TIME_SENSITIVE_ENTITY_KEYS:
self._time_sensitive_remove_listener = self.coordinator.async_add_time_sensitive_listener(
self._handle_time_sensitive_update
)
async def async_will_remove_from_hass(self) -> None:
"""When entity will be removed from hass."""
await super().async_will_remove_from_hass()
# Remove time-sensitive listener if registered
if self._time_sensitive_remove_listener:
self._time_sensitive_remove_listener()
self._time_sensitive_remove_listener = None
@callback
def _handle_time_sensitive_update(self) -> None:
"""Handle time-sensitive update from coordinator."""
# Invalidate cache when data potentially changes
self._cache_key = ""
self._period_cache = {}
self.async_write_ha_state()
def _get_state_getter(self) -> Callable | None:
"""Return the appropriate state getter method based on the sensor type."""
@ -123,6 +158,46 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
return None
def _generate_cache_key(self, *, reverse_sort: bool) -> str:
"""
Generate a cache key based on coordinator data and config options.
This ensures we recalculate when data or configuration changes,
but reuse cached results for multiple property accesses.
"""
if not self.coordinator.data:
return ""
# Include timestamp to invalidate when data changes
timestamp = self.coordinator.data.get("timestamp", "")
# Include relevant config options that affect period calculation
options = self.coordinator.config_entry.options
data = self.coordinator.config_entry.data
if reverse_sort:
flex = options.get(CONF_PEAK_PRICE_FLEX, data.get(CONF_PEAK_PRICE_FLEX, DEFAULT_PEAK_PRICE_FLEX))
min_dist = options.get(
CONF_PEAK_PRICE_MIN_DISTANCE_FROM_AVG,
data.get(CONF_PEAK_PRICE_MIN_DISTANCE_FROM_AVG, DEFAULT_PEAK_PRICE_MIN_DISTANCE_FROM_AVG),
)
min_len = options.get(
CONF_PEAK_PRICE_MIN_PERIOD_LENGTH,
data.get(CONF_PEAK_PRICE_MIN_PERIOD_LENGTH, DEFAULT_PEAK_PRICE_MIN_PERIOD_LENGTH),
)
else:
flex = options.get(CONF_BEST_PRICE_FLEX, data.get(CONF_BEST_PRICE_FLEX, DEFAULT_BEST_PRICE_FLEX))
min_dist = options.get(
CONF_BEST_PRICE_MIN_DISTANCE_FROM_AVG,
data.get(CONF_BEST_PRICE_MIN_DISTANCE_FROM_AVG, DEFAULT_BEST_PRICE_MIN_DISTANCE_FROM_AVG),
)
min_len = options.get(
CONF_BEST_PRICE_MIN_PERIOD_LENGTH,
data.get(CONF_BEST_PRICE_MIN_PERIOD_LENGTH, DEFAULT_BEST_PRICE_MIN_PERIOD_LENGTH),
)
return f"{timestamp}_{reverse_sort}_{flex}_{min_dist}_{min_len}"
def _get_flex_option(self, option_key: str, default: float) -> float:
"""
Get a float option from config entry.
@ -583,19 +658,34 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
]
def _get_price_intervals_attributes(self, *, reverse_sort: bool) -> dict | None:
"""Get price interval attributes with support for 15-minute intervals and period grouping."""
"""
Get price interval attributes with caching to avoid expensive recalculation.
Uses a cache key based on coordinator data timestamp and config options.
Returns simplified attributes without the full intervals list to reduce payload.
"""
# Check cache first
cache_key = self._generate_cache_key(reverse_sort=reverse_sort)
if cache_key and cache_key == self._cache_key and self._period_cache:
return self._period_cache
# Cache miss - perform expensive calculation
if not self.coordinator.data:
return None
price_info = self.coordinator.data.get("priceInfo", {})
yesterday_prices = price_info.get("yesterday", [])
today_prices = price_info.get("today", [])
tomorrow_prices = price_info.get("tomorrow", [])
all_prices = yesterday_prices + today_prices + tomorrow_prices
if not all_prices:
return None
all_prices.sort(key=lambda p: p["startsAt"])
intervals_by_day, avg_price_by_day = self._split_intervals_by_day(all_prices)
ref_prices = self._calculate_reference_prices(intervals_by_day, reverse_sort=reverse_sort)
flex = self._get_flex_option(
CONF_BEST_PRICE_FLEX if not reverse_sort else CONF_PEAK_PRICE_FLEX,
DEFAULT_BEST_PRICE_FLEX if not reverse_sort else DEFAULT_PEAK_PRICE_FLEX,
@ -604,36 +694,103 @@ class TibberPricesBinarySensor(TibberPricesEntity, BinarySensorEntity):
CONF_BEST_PRICE_MIN_DISTANCE_FROM_AVG if not reverse_sort else CONF_PEAK_PRICE_MIN_DISTANCE_FROM_AVG,
DEFAULT_BEST_PRICE_MIN_DISTANCE_FROM_AVG if not reverse_sort else DEFAULT_PEAK_PRICE_MIN_DISTANCE_FROM_AVG,
)
price_context = {
"ref_prices": ref_prices,
"avg_prices": avg_price_by_day,
"flex": flex,
"min_distance_from_avg": min_distance_from_avg,
}
periods = self._build_periods(
all_prices,
price_context,
reverse_sort=reverse_sort,
)
# Filter periods by minimum length requirement
periods = self._build_periods(all_prices, price_context, reverse_sort=reverse_sort)
periods = self._filter_periods_by_min_length(periods, reverse_sort=reverse_sort)
self._add_interval_ends(periods)
# Only use periods relevant for today/tomorrow for annotation and attribute calculation
filtered_periods = self._filter_periods_today_tomorrow(periods)
# Simplified annotation - only annotate enough to find current interval and provide summary
result = self._annotate_period_intervals(
filtered_periods,
ref_prices,
avg_price_by_day,
all_prices,
)
filtered_result = self._filter_intervals_today_tomorrow(result)
current_interval = self._find_current_or_next_interval(filtered_result)
if not current_interval and filtered_result:
current_interval = filtered_result[0]
# Build attributes with current interval info but simplified period summary
attributes = {**current_interval} if current_interval else {}
attributes["intervals"] = filtered_result
# Instead of full intervals list, provide period-level summary
# This reduces the attribute payload by 90%+
if filtered_result:
periods_summary = self._build_periods_summary(filtered_result)
attributes["periods"] = periods_summary
attributes["intervals_count"] = len(filtered_result)
else:
attributes["periods"] = []
attributes["intervals_count"] = 0
# Cache the result
self._cache_key = cache_key
self._period_cache = attributes
return attributes
def _build_periods_summary(self, intervals: list[dict]) -> list[dict]:
"""
Build a summary of periods without including full interval details.
Returns a list of period summaries with key information for automations:
- Period start/end times
- Duration
- Average/min/max prices
- Number of intervals
"""
if not intervals:
return []
# Group intervals by period (they have the same period_start)
periods_dict: dict[str, list[dict]] = {}
for interval in intervals:
period_key = interval.get("period_start")
if period_key:
key_str = period_key.isoformat() if hasattr(period_key, "isoformat") else str(period_key)
if key_str not in periods_dict:
periods_dict[key_str] = []
periods_dict[key_str].append(interval)
# Build summary for each period
summaries = []
for period_intervals in periods_dict.values():
if not period_intervals:
continue
first = period_intervals[0]
prices = [i["price"] for i in period_intervals if "price" in i]
summary = {
"start": first.get("period_start"),
"end": first.get("period_end"),
"hour": first.get("hour"),
"minute": first.get("minute"),
"time": first.get("time"),
"duration_minutes": first.get("period_length_minute"),
"intervals_count": len(period_intervals),
"price_avg": round(sum(prices) / len(prices), 4) if prices else 0,
"price_min": round(min(prices), 4) if prices else 0,
"price_max": round(max(prices), 4) if prices else 0,
}
summaries.append(summary)
return summaries
def _get_price_hours_attributes(self, *, attribute_name: str, reverse_sort: bool) -> dict | None:
"""Get price hours attributes."""
if not self.coordinator.data:

102
custom_components/tibber_prices/coordinator.py
View file

@ -53,6 +53,61 @@ QUARTER_HOUR_BOUNDARIES = (0, 15, 30, 45)
# Hour after which tomorrow's price data is expected (13:00 local time)
TOMORROW_DATA_CHECK_HOUR = 13
# Entity keys that require quarter-hour updates (time-sensitive entities)
# These entities calculate values based on current time and need updates every 15 minutes
# All other entities only update when new API data arrives
TIME_SENSITIVE_ENTITY_KEYS = frozenset(
{
# Current/next/previous price sensors
"current_price",
"next_interval_price",
"previous_interval_price",
# Current/next/previous price levels
"price_level",
"next_interval_price_level",
"previous_interval_price_level",
# Rolling hour calculations (5-interval windows)
"current_hour_average",
"next_hour_average",
"current_hour_price_level",
"next_hour_price_level",
# Current/next/previous price ratings
"price_rating",
"next_interval_price_rating",
"previous_interval_price_rating",
"current_hour_price_rating",
"next_hour_price_rating",
# Future average sensors (rolling N-hour windows from next interval)
"next_avg_1h",
"next_avg_2h",
"next_avg_3h",
"next_avg_4h",
"next_avg_5h",
"next_avg_6h",
"next_avg_8h",
"next_avg_12h",
# Price trend sensors
"price_trend_1h",
"price_trend_2h",
"price_trend_3h",
"price_trend_4h",
"price_trend_5h",
"price_trend_6h",
"price_trend_8h",
"price_trend_12h",
# Trailing/leading 24h calculations (based on current interval)
"trailing_price_average",
"leading_price_average",
"trailing_price_min",
"trailing_price_max",
"leading_price_min",
"leading_price_max",
# Binary sensors that check if current time is in a period
"peak_price_period",
"best_price_period",
}
)
class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
"""Enhanced coordinator with main/subentry pattern and comprehensive caching."""
@ -100,6 +155,11 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
# Quarter-hour entity refresh timer (runs at :00, :15, :30, :45)
self._quarter_hour_timer_cancel: CALLBACK_TYPE | None = None
# Selective listener system for time-sensitive entities
# Regular listeners update on API data changes, time-sensitive listeners update every 15 minutes
self._time_sensitive_listeners: list[CALLBACK_TYPE] = []
self._schedule_quarter_hour_refresh()
def _log(self, level: str, message: str, *args: Any, **kwargs: Any) -> None:
@ -107,6 +167,39 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
prefixed_message = f"{self._log_prefix} {message}"
getattr(_LOGGER, level)(prefixed_message, *args, **kwargs)
@callback
def async_add_time_sensitive_listener(self, update_callback: CALLBACK_TYPE) -> CALLBACK_TYPE:
"""
Listen for time-sensitive updates that occur every quarter-hour.
Time-sensitive entities (like current_price, next_interval_price, etc.) should use this
method instead of async_add_listener to receive updates at quarter-hour boundaries.
Returns:
Callback that can be used to remove the listener
"""
self._time_sensitive_listeners.append(update_callback)
def remove_listener() -> None:
"""Remove update listener."""
if update_callback in self._time_sensitive_listeners:
self._time_sensitive_listeners.remove(update_callback)
return remove_listener
@callback
def _async_update_time_sensitive_listeners(self) -> None:
"""Update all time-sensitive entities without triggering a full coordinator update."""
for update_callback in self._time_sensitive_listeners:
update_callback()
self._log(
"debug",
"Updated %d time-sensitive entities at quarter-hour boundary",
len(self._time_sensitive_listeners),
)
def _schedule_quarter_hour_refresh(self) -> None:
"""Schedule the next quarter-hour entity refresh using Home Assistant's time tracking."""
# Cancel any existing timer
@ -145,11 +238,10 @@ class TibberPricesDataUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
# Entity update already done in _check_and_handle_midnight_turnover
# Skip the regular update to avoid double-update
else:
# Regular quarter-hour refresh - notify listeners to update their state
# This causes entity state properties to be re-evaluated with the current time
# Using async_update_listeners() instead of async_set_updated_data() to avoid
# interfering with the coordinator's update timing
self.async_update_listeners()
# Regular quarter-hour refresh - only update time-sensitive entities
# This causes time-sensitive entity state properties to be re-evaluated with the current time
# Static entities (statistics, diagnostics) only update when new API data arrives
self._async_update_time_sensitive_listeners()
@callback
def _check_and_handle_midnight_turnover(self, now: datetime) -> bool:

27
custom_components/tibber_prices/sensor.py
View file

@ -11,6 +11,7 @@ from homeassistant.components.sensor import (
SensorEntityDescription,
)
from homeassistant.const import PERCENTAGE, EntityCategory
from homeassistant.core import callback
from homeassistant.util import dt as dt_util
from .average_utils import (
@ -41,6 +42,7 @@ from .const import (
get_entity_description,
get_price_level_translation,
)
from .coordinator import TIME_SENSITIVE_ENTITY_KEYS
from .entity import TibberPricesEntity
from .price_utils import (
MINUTES_PER_INTERVAL,
@ -514,6 +516,31 @@ class TibberPricesSensor(TibberPricesEntity, SensorEntity):
self._attr_unique_id = f"{coordinator.config_entry.entry_id}_{entity_description.key}"
self._attr_has_entity_name = True
self._value_getter: Callable | None = self._get_value_getter()
self._time_sensitive_remove_listener: Callable | None = None
async def async_added_to_hass(self) -> None:
"""When entity is added to hass."""
await super().async_added_to_hass()
# Register with coordinator for time-sensitive updates if applicable
if self.entity_description.key in TIME_SENSITIVE_ENTITY_KEYS:
self._time_sensitive_remove_listener = self.coordinator.async_add_time_sensitive_listener(
self._handle_time_sensitive_update
)
async def async_will_remove_from_hass(self) -> None:
"""When entity will be removed from hass."""
await super().async_will_remove_from_hass()
# Remove time-sensitive listener if registered
if self._time_sensitive_remove_listener:
self._time_sensitive_remove_listener()
self._time_sensitive_remove_listener = None
@callback
def _handle_time_sensitive_update(self) -> None:
"""Handle time-sensitive update from coordinator."""
self.async_write_ha_state()
def _get_value_getter(self) -> Callable | None:
"""Return the appropriate value getter method based on the sensor type."""