Add entity_resolver module that lets all service parameters accept
HA entity references in place of literal values. The entity's current
state (or a specific attribute via the @attr syntax) is resolved at
call time and coerced to the expected Python type.
Syntax:
"sensor.washing_duration" → uses entity state
"sensor.washing_duration@run_minutes" → uses entity attribute
Apply or_entity_ref() and resolve_entity_references() to all five
service handlers (get_price, find_cheapest_block, find_cheapest_hours,
find_cheapest_schedule, get_chartdata) for every parameter where a
dynamic value from another entity is useful (duration, start/end times,
offsets, etc.).
Add five new translation keys for entity-resolution error messages
(invalid_entity_reference, entity_not_found, entity_attribute_not_found,
entity_state_unavailable, entity_value_conversion_failed) across all
five language files.
Fix pytest warning filter to suppress AsyncMock cleanup noise, and
update test_resource_cleanup to mock hass.config_entries.async_entries
so the blueprint-removal path in async_remove_entry does not raise.
Impact: Automations and scripts can pass sensor entity IDs as service
parameters (e.g. duration from a sensor) instead of having to use
template-based workarounds.
When sequential: true, tasks are placed in declaration order instead of
being sorted by duration. Each task's search window starts after the
previous task ends (plus gap_minutes). If a task cannot be placed, all
subsequent tasks in the chain are also marked unscheduled.
Adds 12 tests covering ordering, chaining, gap enforcement, and
chain-breaking behavior.
Impact: Users can now schedule dependent appliances (e.g., washing
machine → dryer) in a single find_cheapest_schedule call with guaranteed
order, instead of chaining two find_cheapest_block calls.
Removed unnecessary sections from the get_apexcharts_yaml service and added new fields for search tuning and cost estimation in chartdata services. This improves the clarity and usability of the service definitions.
Impact: Users will benefit from a more concise and organized service structure, enhancing the overall experience.
The old extension algorithm extended a single late-evening period forward
past midnight by appending qualifying intervals one-directionally. This
caused false extensions (e.g. peak 19:45–21:30 extended to 01:00 by
pulling in 14 declining-price intervals).
Replace with bidirectional bridging: two independently qualifying periods
on both sides of midnight are merged only when separated by a small gap
(≤4 intervals = 1 hour) and the combined period passes the CV quality
gate (≤25%). A period ending well before midnight is no longer touched.
User-Impact: none
Implement a new service that progressively relaxes user-defined filters to ensure a result is always returned when price data is available. This includes three phases: halving the minimum distance from average, expanding level filters, and reducing duration.
Impact: Users will receive results even when strict filters would otherwise yield no matches, improving the reliability of scheduling actions.
feat(pricing): enhance scheduling actions with new parameters
Introduce new parameters `smooth_outliers`, `min_distance_from_avg`, and `allow_relaxation` to scheduling actions, allowing for better control over price selection and ensuring results are meaningfully different from average prices.
Impact: Users can now fine-tune their scheduling actions to avoid marginal savings and ensure more uniform pricing within selected windows.
docs(scheduling): update documentation for new features
Revise the scheduling actions documentation to include new parameters and their effects, such as outlier smoothing and minimum distance from average, along with examples for better user understanding.
Impact: Users will have clearer guidance on how to utilize new features effectively in their automations.
test(scheduling): add tests for new relaxation logic
Implement unit tests to verify the behavior of the new relaxation logic in scheduling actions, ensuring that filters are correctly relaxed and results are returned as expected.
Impact: Increased test coverage and reliability of the scheduling features.
Improve the logic for trimming trailing gap-tolerance intervals in periods to prevent misleading period end shifts. Additionally, refine cross-day boundary validation for both best and peak periods to eliminate false extremes caused by inter-day reference shifts.
Impact: More accurate period calculations and reduced artifacts in reported price periods.
Improve the filtering of peak periods to eliminate cross-day artifacts and ensure that only genuine high-price windows are retained. This includes adjustments to the criteria for peak classification and the introduction of validation against previous day's prices for overnight intervals.
Impact: Users will experience more accurate peak pricing data, reducing misleading peak classifications on flat days.
Updated pattern names for clarity and consistency in the codebase. The changes include renaming constants and updating related logic to reflect the new terminology.
Impact: Improved readability and understanding of day pattern classifications for developers.
Refactor the pattern classification logic to include start and end prices for better accuracy in identifying day patterns. This change improves the classification of price patterns, particularly for cases involving valleys and peaks.
Impact: Users will experience more accurate price pattern classifications, leading to better decision-making based on price trends.
The PRICE_INFO endpoint returns all available intervals (~384) regardless
of the requested range. When fetching multiple missing ranges, subsequent
calls are redundant if the first response already covers them.
After each fetch, track returned timestamps and skip ranges that are
already covered by previously fetched data.
Impact: Reduces redundant Tibber API calls, especially after restarts or
cache invalidation when multiple gaps exist in the interval pool.
Touch operations create dead intervals in old fetch groups, but GC only
ran when new intervals were added. Dead intervals accumulated until
the next fetch with genuinely new data.
Now run GC after touch-only paths and schedule a save if data changed.
Impact: Reduces memory usage by cleaning up stale fetch groups promptly
instead of letting dead intervals accumulate between API fetches.
Cherry-pick of v0.30.1 hotfix (ec3bc9f). When _cleanup_dead_intervals
compacted group lists but no groups became fully empty, the index retained
stale interval_index values pointing past compacted list ends, causing
IndexError in _get_cached_intervals.
Now rebuilds the index whenever dead intervals are removed, even if no
groups are deleted.
Includes regression test for Issue #118 and updated touch operation tests
to reflect that GC now runs immediately after touch-only paths.
Closes#118
Impact: Eliminates IndexError crash for users with brand-new Tibber accounts
that have limited price history, where partial group compaction was most likely.
Consolidate logic for determining current price phase and associated attributes by introducing shared helper functions. This enhances code maintainability and reduces duplication across components.
Impact: Improved clarity and efficiency in price phase handling for users.
Revised the descriptions and names for the previous interval price rank entities across multiple language translation files to enhance clarity and consistency.
Impact: Users will see improved terminology in the interface, making it clearer that the ranks refer to the previous interval's prices.
Remove the DATA_STATISTICS_REVIEW_REQUIRED flag and all associated
persistence logic. The flag approach was over-engineered: we cannot
detect whether the Recorder statistics have been fixed, and requiring
the user to re-save display settings as acknowledgement is bad UX.
New design: show the repair notice once when the mode changes.
The user dismisses it when done reviewing. The HA Recorder will
independently show its own unit-change dialog — that is sufficient.
Changes:
- Remove DATA_STATISTICS_REVIEW_REQUIRED constant from const.py
- Remove _check_statistics_review_repair() from __init__.py
- Remove ir import from __init__.py (no longer needed there)
- Remove flag set/clear logic from options_flow.py
- Change is_persistent=False (no restart persistence needed)
- Update all 5 translations: restore simple "Dismiss this notice" ending
We have no way to programmatically detect whether the Recorder statistics
have been fixed. Dismissing the Repairs notification does not mean the
problem is resolved, only that the user has seen it.
Revert to delete + create on every async_setup_entry when the flag is set.
This guarantees the issue is visible after every restart until the user
explicitly acknowledges completion by re-saving the display settings in
the options flow.
Remove the dismissed_version auto-clear logic that was treating dismissal
as acknowledgement (it was not).
Update all 5 translation files: replace "Dismiss this notice" with
instructions to re-save display settings as the only way to permanently
close the notification.
Released-Bug: no
async_create_issue preserves dismissed_version when called with the same
issue_id. This means that if a user had previously dismissed the repair
issue, changing the currency mode again would set the flag but the issue
would stay hidden (because it still has a dismissed_version).
Fix: use delete + create in the options flow when mode_changed=True.
This resets dismissed_version so the new instance is always visible.
The __init__.py path (HA restart with flag set) continues to use plain
async_create_issue so that a restart alone does not un-dismiss an issue
the user already acknowledged.
Released-Bug: no
The previous implementation used delete + create on every async_setup_entry,
which reset dismissed_version and forced the issue to reappear after every
HA restart regardless of whether the user had dismissed it.
Fix: use async_create_issue (internally get-or-create, preserves
dismissed_version when params are unchanged) instead of delete + create.
The options flow still uses delete + create when the mode changes again,
ensuring the issue is forced into view for any new change.
Also auto-clear the DATA_STATISTICS_REVIEW_REQUIRED flag from
config_entry.data when the issue is detected as dismissed on setup,
so the flag does not linger indefinitely after the user has acknowledged
the issue.
Released-Bug: no
_resolve_time_with_day_offset() was calling dt_util.now() internally
instead of using the injected now parameter. This caused incorrect date
calculations in tests and any caller that passes a specific reference time.
Also add missing price_rank_* sensor keys to TIME_SENSITIVE_ENTITY_KEYS
in coordinator/constants.py so quarter-hour refresh is registered for all
11 price rank sensors (current/next/previous interval and hour variants).
Rename dt as dt_utils → dt as dt_util (ICN001) across 11 files to follow
the project-wide import alias convention. Apply ruff auto-fixes for import
ordering and collapsing single-item imports throughout the codebase.
Released-Bug: no
Add DATA_STATISTICS_REVIEW_REQUIRED flag to config_entry.data. Set on
currency mode change, cleared on same-mode save. On every async_setup_entry
with flag set, delete and recreate the repair issue so it reappears after
HA restart even if previously dismissed.
Repair issue text explains that HA Recorder shows its own unit-change
dialog (delayed) and recommends deleting old statistic data rather than
re-labeling, which would leave wrong values with the new unit.
Impact: Users are notified to review statistics and automations after
switching between base/subunit currency mode. Notification persists across
HA restarts until acknowledged by saving display settings again.
Add get_display_precision() to const.py returning DISPLAY_PRECISION_SUBUNIT (2)
or DISPLAY_PRECISION_BASE (4) based on config. Replace hardcoded round(..., 2)
with get_display_precision() in all calculators and attribute builders.
Add _update_suggested_precision() to sensor core; syncs entity registry
suggested_display_precision on every coordinator update.
Interval price sensors get full precision (2 or 4 dp); other MONETARY sensors
get half precision (1 or 2 dp) as sensible default.
Impact: Price sensor states and attributes now correctly use 4 decimal places
in base-currency mode (was always 2). Display precision in dashboards updates
automatically when currency mode changes.
Refactor the contribution guidelines to enhance readability and consistency in formatting. Adjusted code blocks and list formatting for better visual structure.
Impact: Contributors will find it easier to follow the guidelines when contributing to the project.
---
docs(README): update automation examples for better readability
Reformatted YAML automation examples in the README to improve clarity and consistency. Indentation and structure were adjusted for better understanding.
Impact: Users will have clearer examples for setting up automations with the integration.
---
chore(manifest): streamline manifest file formatting
Consolidated formatting in the manifest file for consistency. Adjusted the codeowners and requirements sections for a cleaner look.
User-Impact: none
---
chore(pyproject): enhance project configuration for better linting and testing
Updated the pyproject.toml file to improve linting configurations and testing options. Added specific rules for ruff and pytest to align with project standards.
User-Impact: none
---
chore(manifest_schema): simplify JSON schema for integration manifest
Refined the manifest schema by consolidating enum definitions for better readability and maintenance.
User-Impact: none
---
chore(prettier): add Prettier configuration for consistent code formatting
Introduced a Prettier configuration file to standardize code formatting across the project, ensuring consistency in style.
User-Impact: none
Updated color and icon logic to include additional keys for price trends, outlooks, and trajectories. This improves the visual representation of price changes in the UI.
Impact: Users will see more accurate color coding and icons for price trends and forecasts.
Improve error handling for API fetch failures by implementing a fallback to cached intervals. This ensures the system can continue functioning during transient API issues.
Impact: Users experience fewer interruptions when the API is temporarily unavailable, as cached data will be used seamlessly.
Modified error messages in multiple language translation files to remove unnecessary curly braces around the template placeholder for improved clarity.
Impact: Users will see clearer error messages when invalid array_fields are provided.
Improved validation logic for service parameters in find_cheapest_hours, find_cheapest_schedule, and chartdata services. Added checks for unique task names, ensured that segment durations do not exceed total duration, and clarified error messages for better user understanding.
Impact: Users will receive clearer error messages and improved validation when using the services, leading to a more robust experience.
Updated the filter logic to include period_filter alongside level_filter and rating_level_filter for segment definitions. This change ensures that users can utilize period_filter effectively when defining segments.
Impact: Users can now use period_filter in addition to existing filters for more flexible segment definitions.
Updated sensor definitions to enhance clarity and maintain consistency by removing the diagnostic entity category from day pattern sensors.
Impact: No user-facing changes.
Updated the long descriptions and usage tips for the price trend change sensors in multiple languages (de, en, nb, nl, sv) to provide clearer guidance on detection mechanics and expected behavior during V-shaped price days.
Impact: Users will have a better understanding of how the sensors operate and can make more informed decisions regarding automation based on price trends.
Refactor the period extension logic to clarify the handling of primary and fallback price levels. Update the documentation to reflect the changes in how periods extend into adjacent intervals.
Impact: Users will benefit from clearer price extension behavior and improved performance in period calculations.
Replace CV with IQR% as the primary indicator for flat-day detection
in _compute_day_effective_min(). CV is inflated by isolated price spikes
(a single spike at 2× the average pushes CV to 15-25% while the core
price band stays flat), causing the flat-day adaptation to be missed.
IQR% (spread of the central 50% of prices / median) is unaffected by
tail outliers and correctly identifies "flat core + spike" days.
Threshold: LOW_IQR_PCT_FLAT_DAY_THRESHOLD = 15.0%
- IQR% ≈ 1.35 × CV for symmetric data, so 15% ≈ old CV threshold of 10%
- Extra headroom catches flat days with a single outlier (IQR%~3%,
CV~20%) that were previously missed
CV retained as fallback for edge cases where iqr_pct is None
(near-zero or negative median prices).
Impact: Flat days with a single isolated price spike are now correctly
identified, reducing unnecessary relaxation iterations on those days.
Rename the three existing price rank sensors from price_rank_* to
current_interval_price_rank_* to clarify they rank the current
quarter-hour interval's price, not a daily aggregate — consistent with
current_interval_price_level / current_interval_price_rating naming.
Add 8 new rank sensors covering additional subjects and reference windows:
- next_interval_price_rank_{today,today_tomorrow}
- previous_interval_price_rank_{today,today_tomorrow}
- current_hour_price_rank_{today,today_tomorrow} (5-interval rolling avg)
- next_hour_price_rank_{today,today_tomorrow} (5-interval rolling avg)
All new sensors are disabled by default. The volatility calculator gains a
subject parameter (_get_subject_price / _get_subject_price_attr_key /
_get_rolling_hour_avg_price) to select which price to rank. Sensor key
routing in value_getters.py and attributes/__init__.py updated accordingly.
No migration entries needed — the original price_rank_* sensors were never
released to users.
All 5 translation files updated. sensor-reference.md regenerated (129 entities).
Impact: Users can now track price rank for the next interval (look-ahead),
the previous interval (logging), and rolling hourly averages — for both
same-day and two-day reference windows.
Add entity descriptions, long descriptions, and usage tips for the three new
price_rank_* sensors and the updated volatility sensors with IQR attributes.
Plain-language terms are used as primary labels (e.g. "typical price band",
"price rank"); technical terms are included parenthetically for experts
(e.g. "IQR", "percentile rank", "Tukey fences") in all five languages.
Impact: Sensors show descriptive help text in the entity detail view, making it
easier for users to understand what each sensor measures without consulting
external documentation.
Add entity name translations for price_rank_today, price_rank_tomorrow, and
price_rank_today_tomorrow sensors in English, German, Norwegian, Dutch, and
Swedish.
Impact: Sensor display names appear correctly in the Home Assistant UI for all
supported languages.
Add three new price rank sensors that show where today's/tomorrow's/combined
average price falls relative to all intervals in the evaluated window:
- price_rank_today: today's average price percentile rank (0–100%)
- price_rank_tomorrow: tomorrow's average price percentile rank
- price_rank_today_tomorrow: combined today+tomorrow percentile rank
Extend all volatility sensors with IQR-based band statistics:
- price_typical_spread: interquartile range (IQR) in currency subunit
- price_typical_spread_%: IQR as percentage of daily average
- price_spike_count: number of intervals outside Tukey fences (outliers)
Add calculate_iqr_stats() utility function in utils/price.py that computes
the 25th/75th percentiles, IQR, outer fences (Q1 - 1.5×IQR / Q3 + 1.5×IQR),
and outlier count for any list of price values. Entity keys and attribute
names use plain language (`price_rank`, `price_typical_spread`) as primary
labels; technical terms (percentile rank, IQR) are included parenthetically
in descriptions and documentation.
Impact: Users can now see where current day prices rank compared to their window and how tightly clustered or spike-prone a day's prices are.
Add structured reason codes to no-result responses for find_cheapest_block,
find_cheapest_hours, and find_cheapest_schedule. Each handler now classifies
why no result was returned: no_data_in_range, no_intervals_matching_level_filter,
insufficient_intervals_after_filter, or insufficient_contiguous_window.
Add include_comparison_details flag to find_cheapest_schedule. When enabled,
each scheduled task includes a price_comparison field showing the most expensive
alternative window (mean, min, max, start, end) for cost-savings context.
Document stable reason code contracts in en.json service descriptions.
Add corresponding field translations to all locales (de, nb, nl, sv).
Impact: Automations and scripts can now react to why no window was found,
and schedules can display concrete savings vs. worst-case pricing.
Consistent naming with the period_count_* family introduced in the
previous commit (period_count_total, period_count_today,
period_count_tomorrow).
periods_remaining was the last attribute in the navigation triplet
using the old plural form. Renamed to period_count_remaining to follow
the established pattern: all countable period metrics use the
period_count_* prefix.
BREAKING CHANGE: periods_remaining renamed to period_count_remaining.
Impact: All four period count attributes now share the same prefix
(period_count_total, period_count_today, period_count_tomorrow,
period_count_remaining), making automation templates more predictable.
Removed periods_found_total and replaced with period_count_today /
period_count_tomorrow. The old attribute counted all periods including
yesterday (coordinator scope), causing a discrepancy vs. the displayed
list (sensor scope, today+tomorrow only).
Renamed periods_total → period_count_total for naming consistency with
the new per-day attributes. Recalculate period_position / period_count_total /
periods_remaining after the today+tomorrow filter so all three navigation
attributes reflect the filtered scope.
period_count_tomorrow is always present (0 when no tomorrow data or no
periods found), enabling automations without default(0) guards.
Removed internal periods_found key from relaxation metadata — it was
only consumed by add_calculation_summary_attributes which is now removed.
BREAKING CHANGE: periods_found_total removed (replace with
period_count_today + period_count_tomorrow). periods_total renamed to
period_count_total.
Impact: Period navigation attributes (position/total/remaining) now
correctly reflect today+tomorrow scope. Per-day counts allow automations
to distinguish "2 periods today, 0 tomorrow" from "1+1".
Phase 3: When geometric bonus intervals cause CV gate failure, strip them
from period edges (unextended boundaries) and set geometric_extension_attempted=True
on the summary. Previously only geometric_extension_active was tracked.
Moved LOW_PRICE_QUALITY_BYPASS_THRESHOLD constant to types.py for shared access.
Phase 4: Add time_range: tuple[datetime, datetime] | None parameter to
build_periods(), calculate_periods(), and calculate_periods_with_relaxation().
Filters candidate intervals to [start, end) without affecting day-wide reference
prices. Refactored _apply_segment_forcing() to use time_range instead of the
restricted_prices list approach.
Impact: Period statistics now accurately reflect when geometric flex extension
was attempted but reverted due to quality gate failure. Segment forcing uses
a cleaner API that preserves full price context for reference calculations.
Phase 3: When geometric bonus intervals cause CV gate failure, strip them
from period edges (unextended boundaries) and set geometric_extension_attempted=True
on the summary. Previously only geometric_extension_active was tracked.
Moved LOW_PRICE_QUALITY_BYPASS_THRESHOLD constant to types.py for shared access.
Phase 4: Add time_range: tuple[datetime, datetime] | None parameter to
build_periods(), calculate_periods(), and calculate_periods_with_relaxation().
Filters candidate intervals to [start, end) without affecting day-wide reference
prices. Refactored _apply_segment_forcing() to use time_range instead of the
restricted_prices list approach.
Impact: Period statistics now accurately reflect when geometric flex extension
was attempted but reverted due to quality gate failure. Segment forcing uses
a cleaner API that preserves full price context for reference calculations.
Uses valley/peak knee points from day pattern analysis to grant extra
flex to price intervals that fall inside detected geometric zones,
making period detection more permissive within V-shape (best price)
or Λ-shape (peak price) price formations.
New options:
- CONF_BEST_PRICE_GEOMETRIC_FLEX (0-25%, default 0 = disabled)
- CONF_PEAK_PRICE_GEOMETRIC_FLEX (0-25%, default 0 = disabled)
Implementation:
- compute_geometric_flex_bonus() in level_filtering.py checks if
interval falls inside valley/peak zone and returns extra_flex
- period_building.py applies geo bonus per-interval via
criteria._replace(flex=...) and sets geometric_bonus_applied flag
- period_statistics.py reports geometric_extension_active and
geometric_extension_intervals in period summaries
- Day patterns threaded through full pipeline:
data_transformation → coordinator/core → periods →
relaxation → calculate_periods → price_context
- UI sliders in both extension_settings sections
- Translations: en, de, nb, nl, sv
Impact: Users with clearly V-shaped or Λ-shaped daily price curves
can enable geometric flex to improve period detection accuracy within
those characteristic shapes without increasing global flex.
After period detection, optionally walk left/right from each period boundary
to absorb adjacent VERY_CHEAP (best price) or VERY_EXPENSIVE (peak price)
intervals (step 7.5 in the pipeline).
New constants: CONF_BEST_PRICE_EXTEND_TO_VERY_CHEAP, CONF_BEST_PRICE_MAX_EXTENSION_INTERVALS,
CONF_PEAK_PRICE_EXTEND_TO_VERY_EXPENSIVE, CONF_PEAK_PRICE_MAX_EXTENSION_INTERVALS.
Defaults: off / 4 intervals (1 hour per side). Hard maximum: 12 intervals (3 hours).
Config stored under "extension_settings" section, reflected in period hash
for correct cache invalidation.
New module: coordinator/period_handlers/shape_extension.py handles the
boundary walk, stat recalculation, and extension_intervals_added bookkeeping.
Impact: Users can opt-in to wider best/peak price windows that include
extreme-level adjacent intervals, reducing missed very cheap/expensive slots
at period edges.
Introduces a new day_pattern.py module that analyses the 15-min price curve
for each calendar day (yesterday/today/tomorrow) and classifies its shape.
New sensors:
day_pattern_yesterday / day_pattern_today / day_pattern_tomorrow
EntityCategory.DIAGNOSTIC, SensorDeviceClass.ENUM
Patterns: valley, peak, double_valley, double_peak, flat, rising, falling, mixed
The detector uses centred-rolling smoothing, prominence-filtered extrema,
Kneedle-based knee detection, and monotone segment building.
Coordinator populates transformed_data["dayPatterns"] after priceInfo enrichment.
Impact: Users can trigger automations based on the shape of the day's price
curve, e.g. pre-heat when tomorrow is a valley day.
