_get_sensor_interval_stats() computed expected_count via UTC time
arithmetic ((end - start).total_seconds() / 900 = 480 for 5 days), then
iterated through fixed-offset local timestamps adding timedelta(minutes=15).
On DST spring-forward days (e.g. last Sunday March in EU), clocks skip
from 02:00 to 03:00. The 4 local quarter-hour slots 02:00-02:45 never
exist, so the Tibber API never returns intervals for them. The iteration
still visits those 4 keys, finds them absent from the index, and reports
has_gaps=True (expected=480, actual=476). Since no API call can ever
fill those non-existent slots, the pool triggers an unnecessary gap-fill
fetch every 15 minutes for the entire spring-forward day.
Fix: keep the nominal expected_count for diagnostics, but determine
has_gaps via the new _has_real_gaps_in_range() helper that sorts
cached intervals by UTC time and checks consecutive UTC differences.
The 01:45+01:00 -> 03:00+02:00 transition is exactly 15 minutes in
UTC, so no gap is reported. Start/end boundary comparisons use naive
19-char local timestamps to stay consistent with the fixed-offset
arithmetic used by get_protected_range().
Impact: No spurious API fetches on DST spring-forward Sunday. Gap
detection for real missing data (API failures, first startup) remains
fully functional.
All three re-transform sites (_handle_options_update,
async_handle_config_override_update, _perform_midnight_data_rotation)
were building raw_data with only {'price_info': ...}, omitting
'currency' and 'home_id'.
data_transformation.py's transform_data() falls back to currency='EUR'
and home_id='' when those keys are missing. This caused all non-EUR
users (Norway/NOK, Sweden/SEK) to see wrong currency units in sensors
after every midnight turnover and after any options/override change,
until the next full API poll refilled coordinator.data with correct
values (up to 15 minutes of wrong units).
Fix: explicitly carry over coordinator.data's existing currency and
home_id into each raw_data dict. Also inline the redundant lambda
wrapper on calculate_periods_fn (PLW0108 ruff lint).
Impact: Norwegian and Swedish users no longer see EUR/ct units after
midnight or config changes. Sensor unit-of-measurement stays consistent
throughout the day regardless of re-transform triggers.
All entity descriptions had hardcoded English name= strings that were
never used at runtime: HA always prefers translations via translation_key
(entity.<platform>.<key>.name in translations/en.json), making the name=
fields dead code.
Removed 106 lines across all four platforms:
- sensor/definitions.py: 85 name= lines
- number/definitions.py: 12 name= lines
- binary_sensor/definitions.py: 6 name= lines
- switch/definitions.py: 3 name= lines
No functional change. The unique_id (entry_id + key) and translation_key
remain stable, ensuring entity IDs and friendly names are unaffected.
Impact: Cleaner definitions, no drift between name= strings and
translations. Aligns with HA standard: translations are the single
source of truth for entity names.
Add coverage for the state_class/statistics table optimization across
both user and developer documentation.
docs/user/docs/configuration.md:
- Add 'Price Sensor Statistics' section explaining that only 3 sensors
write to the HA statistics database (current_interval_price,
current_interval_price_base, average_price_today)
- Fix incorrect entity ID examples: remove non-existent _override suffix
from recorder exclude globs, Developer Tools example, and seasonal
automation example (actual IDs: number.*_best_price_flexibility etc.)
docs/developer/docs/recorder-optimization.md:
- Add 'Long-Term Statistics Optimization (state_class)' section covering
the statistics/statistics_short_term table dimension, which is distinct
from _unrecorded_attributes (state_attributes table)
- Documents the MONETARY device_class constraint (MEASUREMENT blocked by
hassfest, only TOTAL or None valid), the 3 sensors keeping TOTAL with
rationale, the 23 sensors set to None, and ~88% write reduction
- Includes comparison table: _unrecorded_attributes vs state_class
Impact: Users now understand the built-in statistics optimization and
have correct recorder exclude examples. Developers understand both
optimization layers and their interaction.
Previously all 26 MONETARY sensors had state_class=TOTAL, causing the
statistics and statistics_short_term tables to grow unbounded (never
auto-purged by HA).
Reduced to 3 sensors that genuinely benefit from long-term history:
- current_interval_price (main price sensor, trend over weeks/months)
- current_interval_price_base (required for Energy Dashboard)
- average_price_today (daily avg tracking over seasons)
Set state_class=None on 23 sensors where long-term history adds no
value: forecast/future sensors (next_avg_*h), daily snapshots
(lowest/highest_price_today), tomorrow sensors, rolling windows
(trailing/leading 24h), and next/previous interval sensors.
Note: state_class=None does not affect the States timeline (History
panel). Only the Statistics chart on entity detail pages is removed
for the affected sensors. Existing statistics data is retained.
Impact: ~88% reduction in statistics table writes. Prevents database
bloat reported by users with long-running installations.
User docs (period-calculation.md):
- New troubleshooting section "Fewer Periods Than Configured" (most
common confusing scenario, added before "No Periods Found")
- Extended "Understanding Sensor Attributes" section: all four diagnostic
attributes documented with concrete YAML examples and prose explanations
- Updated Table of Contents
Developer docs (period-calculation-theory.md):
- New section "Flat Day and Low-Price Adaptations" (between Flex Limits
and Relaxation Strategy) documenting all three mechanisms with
implementation details, scaling tables, and rationale for hardcoding
- Two new scenarios: 2b (flat day with adaptive min_periods) and 2c
(solar surplus / absolute low-price day) with expected logs and
sensor attribute examples
- Debugging checklist point 6: flat day / low-price checks with
exact log string patterns to grep for
- Diagnostic attribute reference table in the debugging section
Impact: Users can self-diagnose "fewer periods than configured" without
support. Contributors understand why the three thresholds are hardcoded
and cannot be user-configured.
Add calculation summary attributes to best_price_period and
peak_price_period binary sensors for diagnostic transparency.
New attributes (all excluded from recorder history):
- min_periods_configured: User's configured target per day (always shown)
- periods_found_total: Actual periods found across all days (always shown)
- flat_days_detected: Days where CV <= 10% reduced target to 1 (only when > 0)
- relaxation_incomplete: Some days couldn't reach the target (only when true)
These attributes explain observed behavior without requiring users to
read logs: seeing "flat_days_detected: 1" alongside
"min_periods_configured: 2, periods_found_total: 1" immediately
explains why the count is lower than configured on uniform-price days.
Implementation:
- _compute_day_effective_min() now returns (dict, int) tuple to propagate
the flat day count through to the metadata dict
- flat_days_detected added to metadata["relaxation"] in
calculate_periods_with_relaxation()
- build_final_attributes_simple() gains optional period_metadata parameter
- New add_calculation_summary_attributes() function handles the rendering
- Attributes are shown even when no period is currently active
Updated recorder-optimization.md: attribute counts + clarified that
timestamp is correctly excluded (entity native_value is recorded
separately by HA as the entity state itself).
Impact: Users can understand why they received fewer periods than
configured without enabling debug logging.
Three complementary fixes for pathological price days:
1. Adaptive min_periods for flat days (CV ≤ 10%):
On days with nearly uniform prices (e.g. solar surplus), enforcing
multiple distinct cheap periods is geometrically impossible.
_compute_day_effective_min() detects CV ≤ LOW_CV_FLAT_DAY_THRESHOLD
and reduces the effective target to 1 for that day (best price only;
peak price always runs full relaxation).
2. min_distance scaling on absolute low-price days:
When the daily average drops below 0.10 EUR (10 ct), percentage-based
min_distance becomes unreliable. The threshold is scaled linearly to
zero so the filter neither accepts the entire day nor blocks everything.
3. CV quality gate bypass for absolute low-price periods:
Periods with a mean below 0.10 EUR may show high relative CV even
though the absolute price differences are fractions of a cent.
Both _check_period_quality() and _check_merge_quality_gate() now
bypass the CV gate below this threshold.
Additionally: span-aware flex warnings now emit INFO/WARNING when
base_flex >= 25%/30% and at least one "normal" (non-V-shape) day
exists (FLEX_WARNING_VSHAPE_RATIO = 0.5). Previously the constants
were defined but never used.
Updated 3 test assertions in test_best_price_e2e.py: the flat-day
fixture (CV ~5.4%) correctly produces 1 period, not 2.
Impact: Best Price periods now appear reliably on V-shape solar days
and flat-price days. No more "0 periods" on days where the single
cheapest window is a valid and useful result.
homeassistant==2026.3.4 requires Python>=3.14.2. The lint workflow was
specifying Python 3.13, and uv venv was ignoring actions/setup-python and
picking up the system Python (3.14.0) instead.
Changes:
- lint.yml: python-version 3.13 → 3.14
- bootstrap: uv venv now uses $(which python) to respect
actions/setup-python and local pyenv/asdf setups
Impact: lint workflow no longer fails with Python version unsatisfiable
dependency error when installing homeassistant.
grep -q "refs/tags/$TAG" matched substrings, so v0.27.0b0
would block release of v0.27.0. Changed to "refs/tags/${TAG}$"
to require exact end-of-line match.
_get_cached_intervals() used fixed-offset datetimes from fromisoformat()
for iteration. When start and end boundaries span a DST transition (e.g.,
+01:00 CET → +02:00 CEST), the loop's end check compared UTC values,
stopping 1 hour early on spring-forward days.
This caused the last 4 quarter-hourly intervals of "tomorrow" to be
missing, making the binary sensor "Tomorrow data available" show Off
even when full data was present.
Changed iteration to use naive local timestamps, matching the index key
format (timezone stripped via [:19]). The end boundary comparison now
works correctly regardless of DST transitions.
Impact: Binary sensor "Tomorrow data available" now correctly shows On
on DST spring-forward days. Affects all European users on the last
Sunday of March each year.
When runtime config override entities (number/switch) are enabled,
the Options Flow now displays warning indicators at the top of each
affected section. Users see which fields are being managed by config
entities and can still edit the base values if needed.
Changes:
- Add ConstantSelector warnings in Best Price/Peak Price sections
- Implement multi-language support for override warnings (de, en, nb, nl, sv)
- Add _get_override_translations() to load translated field labels
- Add _get_active_overrides() to detect enabled override entities
- Extend get_best_price_schema/get_peak_price_schema with translations param
- Add 14 number/switch config entities for runtime period tuning
- Document runtime configuration entities in user docs
Warning format adapts to overridden fields:
- Single: "⚠️ Flexibility controlled by config entity"
- Multiple: "⚠️ Flexibility and Minimum Distance controlled by config entity"
Impact: Users can now dynamically adjust period calculation parameters
via Home Assistant automations, scripts, or dashboards without entering
the Options Flow. Clear UI indicators show which settings are currently
overridden.
Add visual indicators to distinguish hourly aggregated data from
original 15-minute interval data in ApexCharts output.
Changes:
- Chart title: Append localized suffix like "(Ø hourly)" / "(Ø stündlich)"
- Y-axis label: Append "(Ø)" suffix, e.g., "øre/kWh (Ø)"
The suffix pattern avoids confusion with Scandinavian currency symbols
(øre/öre) which look similar to the average symbol (Ø) when used as prefix.
Added hourly_suffix translations for all 5 languages (en, de, sv, nb, nl).
Impact: Users can now clearly see when a chart displays averaged hourly
data rather than original 15-minute prices.
Implement clickable legend items to show/hide best/peak price period
overlays in generated ApexCharts YAML configuration.
Legend behavior by configuration:
- Only best price: No legend (overlay always visible)
- Only peak price: Legend shown, peak toggleable (starts hidden)
- Both enabled: Legend shown, both toggleable (best visible, peak hidden)
Changes:
- Best price overlay: in_legend only when peak also enabled
- Peak price overlay: always in_legend with hidden_by_default: true
- Enable experimental.hidden_by_default when peak price active
- Price level series (LOW/NORMAL/HIGH): hidden from legend when
overlays active, visible otherwise (preserves easy legend enable)
- Add triangle icons (▼/▲) before overlay names for visual distinction
- Custom legend markers (size: 0) only when overlays active
- Increased itemMargin for better visual separation
Impact: Users can toggle best/peak price period visibility directly
in the chart via legend click. Without overlays, legend behavior
unchanged - users can still enable it by setting show: true.
Add resolution parameter to get_chartdata and get_apexcharts_yaml services,
allowing users to choose between original 15-minute intervals or aggregated
hourly values for chart visualization.
Implementation uses rolling 5-interval window aggregation (-2, -1, 0, +1, +2
around :00 of each hour = 60 minutes total), matching the sensor rolling
hour methodology. Respects user's CONF_AVERAGE_SENSOR_DISPLAY setting for
mean vs median calculation.
Changes:
- formatters.py: Add aggregate_to_hourly() function preserving original
field names (startsAt, total, level, rating_level) for unified processing
- get_chartdata.py: Pre-aggregate data before processing when resolution is
'hourly', enabling same code path for filters/insert_nulls/connect_segments
- get_apexcharts_yaml.py: Add resolution parameter, pass to all 4 get_chartdata
service calls in generated JavaScript
- services.yaml: Add resolution field with interval/hourly selector
- icons.json: Add section icons for get_apexcharts_yaml fields
- translations: Add highlight_peak_price and resolution field translations
for all 5 languages (en, de, sv, nb, nl)
Impact: Users can now generate cleaner charts with 24 hourly data points
instead of 96 quarter-hourly intervals. The unified processing approach
ensures all chart features (filters, null insertion, segment connection)
work identically for both resolutions.
Added dynamic warnings when users configure settings for sensors that
are currently disabled. This improves UX by informing users that their
configuration changes won't have any visible effect until they enable
the relevant sensors.
Changes:
- Created entity_check.py helper module with sensor-to-step mappings
- Added check_relevant_entities_enabled() to detect disabled sensors
- Integrated warnings into 6 options flow steps (price_rating,
price_level, best_price, peak_price, price_trend, volatility)
- Made Chart Data Export info page content-aware: shows configuration
guide when sensor is enabled, shows enablement instructions when disabled
- Updated all 5 translation files (de, en, nb, nl, sv) with dynamic
placeholders {entity_warning} and {sensor_status_info}
Impact: Users now receive clear feedback when configuring settings for
disabled sensors, reducing confusion about why changes aren't visible.
Chart Data Export page now provides context-appropriate guidance.
