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.
Revised the comment regarding explicitly skipped commits to enhance readability and maintain consistency in documentation style.
Impact: Improved clarity for developers reviewing the release notes generation script.
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.
Implement an auto-assign workflow to automatically assign newly opened issues to the repository owner.
Impact: Streamlines issue management by ensuring the owner is automatically assigned to new issues.
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.
Updated the configuration files to improve development experience by explicitly loading useful integrations and adjusting logging levels. Added YAML schemas for configuration and services to ensure proper structure and validation.
Impact: Developers will have a more streamlined setup process and better logging during integration development.
Enhance the configuration for the HTTP component to support development in Codespaces and DevContainer. This includes settings for server host, IP banning, trusted proxies, and CORS.
Impact: Improved development experience by allowing easier access and configuration in development environments.
Convert four-space-indented list items (`- item`) to standard two-space
(`- item`) in AGENTS.md, CONTRIBUTING.md, README.md, and all Docusaurus
documentation pages (developer and user, including versioned snapshots).
No content changes.
Release-Notes: skip
Apply consistent 4-space indentation and trailing-operator style for line
continuations (&&, |) across all development and release scripts. No logic
changes.
Release-Notes: skip
Update sensors-volatility.md to cover the three new price rank sensors and the
IQR-based volatility attributes (typical price band / price spike count).
Section headers include technical terms in parentheses for experts:
"Typical Price Band Statistics (IQR)" and "Price Rank Sensors (Percentile Rank)".
Attribute tables list Tukey fence formulas and plain-language explanations
side-by-side.
Regenerate sensor-reference.md to include price_rank_today,
price_rank_tomorrow, and price_rank_today_tomorrow with translations for all
five supported languages.
Impact: Users have full documentation for the new sensors including examples,
formulas, and a multi-language lookup table.
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.
cliff.toml:
- Extract Impact footer value from commit footers and use as release note
text when present, falling back to scope+message format otherwise
- Fix whitespace in body template (remove extra indentation)
scripts/release/generate-notes:
- Add RELEASE_NOTES_TRAILER_SKIP_FILTER to exclude commits marked with
Release-Notes: skip, User-Impact: none, or Released-Bug: no trailers
- Add RELEASE_NOTES_COMPACT_DIFF and RELEASE_NOTES_DIFF_MAX_BYTES to
limit AI diff context size for faster, more focused prompts
- Add RELEASE_NOTES_CLIFF_FILTER_PATHS to restrict cliff to user-facing
paths only when generating AI-assisted notes
- Add RELEASE_NOTES_CLIFF_SINGLE_RELEASE to pass --latest to cliff
- Define USER_FACING_PATHS list for scoped AI diff context
Release-Notes: skip
User-Impact: none
AGENTS.md:
- Replace the minimal 'Type checking and linting' block with a full
script selection guide including preferred dev flow (auto-healing),
check-only flow, and agent behavior rules for fix vs. verify intent
- Add new scripts: format-all, lint-fix, lint-all, check-all
- Clarify commit execution rules: agents only run git commit on explicit
user request; a one-time request does not authorize future commits;
git push is never suggested or executed
CONTRIBUTING.md:
- Add reference to commit-messages.instructions.md for full commit rules
- Add trailer examples for suppressing release notes on internal fixes
Release-Notes: skip
User-Impact: none
- Link commit-messages.instructions.md via commitMessageGeneration.instructions
setting so the VS Code SCM Generate button applies project commit rules
- Add explicit editor.formatOnSave: true to [jsonc] language block,
matching the existing [json] block for consistent behavior
Release-Notes: skip
User-Impact: none
Add .github/instructions/commit-messages.instructions.md with Conventional
Commit rules, Impact footer guidance, and release-notes skip trailers.
Wired to VS Code via github.copilot.chat.commitMessageGeneration.instructions
in devcontainer.json so the SCM Generate button uses these rules.
Release-Notes: skip
User-Impact: none
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.
Wrap most user-guide code examples in collapsible details sections to reduce visual noise while keeping Mermaid diagrams expanded.
Standardized summary labels across the user docs to a small set of readable patterns and removed technical or awkward wording from visible collapse titles.
Impact: User documentation is easier to scan, with long examples hidden by default and more consistent expand/collapse labels.
New comprehensive documentation page for all 5 scheduling services:
- Decision flowchart for choosing the right service
- Detailed parameter reference with examples for each service
- Response format examples with realistic data
- Practical automation examples (overnight scheduling, EV charging,
peak price avoidance)
- Power profile and search range explanations
Also updated:
- actions.md: Added scheduling services overview, entry_id as optional
- automation-examples.md: Cross-reference to scheduling services guide
- Sidebar: Added scheduling-services to Reference category
Impact: Users have comprehensive documentation with a decision guide,
practical examples, and automation templates for the new services.
New comprehensive documentation page for all 5 scheduling services:
- Decision flowchart for choosing the right service
- Detailed parameter reference with examples for each service
- Response format examples with realistic data
- Practical automation examples (overnight scheduling, EV charging,
peak price avoidance)
- Power profile and search range explanations
Also updated:
- actions.md: Added scheduling services overview, entry_id as optional
- automation-examples.md: Cross-reference to scheduling services guide
- Sidebar: Added scheduling-services to Reference category
Impact: Users have comprehensive documentation with a decision guide,
practical examples, and automation templates for the new services.
New services for finding optimal electricity price windows:
- find_cheapest_block: Cheapest contiguous time block (e.g., dishwasher)
- find_cheapest_hours: Cheapest N hours, non-contiguous (e.g., EV charging)
- find_cheapest_schedule: Multi-task scheduling with no-overlap (e.g., shared circuit)
- find_most_expensive_block: Most expensive contiguous block (peak avoidance)
- find_most_expensive_hours: Most expensive N hours (consumption shifting)
Key features:
- Flexible search range (today, tomorrow, today+tomorrow, rolling window)
- Power profile support for variable consumption patterns
- Price level filtering (e.g., only CHEAP/VERY_CHEAP intervals)
- Comparison details showing savings vs. alternatives
- Sliding window algorithm (O(n)) for block search, greedy scheduling
for multi-task optimization
Also includes:
- Shared validation utilities (search range, price level, power profile)
- entry_id now optional on all services (auto-selects single home)
- Input validation for existing services (time range, filter conflicts)
- Service icons for all new and existing services
- Translations for all 5 languages (en, de, nb, nl, sv)
- Removed 10 unused config.error translation keys (replaced by exceptions)
- Tests for price window algorithms and search range resolution
Impact: Users can find optimal time windows for appliances, EV charging,
and multi-device scheduling via HA service calls. Existing services
improved with optional entry_id and better input validation.
Remove unused functions, constants, and entity definitions that were
left over from previous refactorings. All removed code was either
superseded by better implementations or never actually called.
Removed functions:
- entity_utils/helpers.py: translate_level(), translate_rating_level()
(HA handles ENUM translation automatically via translations/*.json)
- entity_utils/attributes.py: build_timestamp_attribute(),
build_period_attributes() (superseded by inline implementations)
- sensor/helpers.py: get_hourly_price_value(), aggregate_window_data()
(replaced by Calculator Pattern in sensor/calculators/)
Removed constants and definitions:
- const.py: CONF_CHART_DATA_CONFIG (DATA_CHART_CONFIG is the active one),
PRICE_LEVEL_OPTIONS, PRICE_RATING_OPTIONS, VOLATILITY_OPTIONS,
PRICE_TREND_OPTIONS (never imported; options defined inline in
definitions.py due to HA import timing constraints),
async_get_home_type_translation() (sync version used instead)
- coordinator/core.py: FRESH_TO_CACHED_SECONDS (leftover from old
caching strategy, never referenced)
- switch/definitions.py: BEST_PRICE_SWITCH_ENTITIES (duplicate of
BEST_PRICE_SWITCH_ENTITY_DESCRIPTIONS using base class instead of
custom TibberPricesSwitchEntityDescription subclass)
Cleanup:
- entity_utils/__init__.py: Remove exports for deleted functions
- sensor/helpers.py: Remove now-unused imports (timedelta,
get_intervals_for_day_offsets, get_price_value, Callable)
- entity_utils/helpers.py: Remove unused get_price_level_translation
import after translate_level() removal
- sensor/definitions.py: Update 7x "Keep in sync with *_OPTIONS"
comments to reference individual PRICE_LEVEL_*/PRICE_RATING_*/
VOLATILITY_* constants instead
Impact: No user-visible changes. Reduces codebase by ~130 lines.
Improves maintainability by eliminating misleading dead code.
Disable autoCollapseCategories to prevent unwanted collapsing when
clicking an active category header twice.
Add hideOnScroll to navbar for more reading space on long pages.
Add category link targets in both sidebars so category headers are
clickable and navigate to the section overview page.
Impact: Sidebar navigation no longer collapses unexpectedly.
Category titles are now direct navigation links. Navbar hides while
scrolling, giving more screen space for content.
Add UP037 to ruff ignore list to preserve quoted TYPE_CHECKING forward
references (PEP 649 lazy eval breaks get_type_hints() at runtime for
TYPE_CHECKING-guarded imports).
Move datetime imports into TYPE_CHECKING blocks in sensor/calculators
timing.py and trend.py (TC003, type-only usage confirmed).
Apply PEP 758 parenthesis-free except clauses across 7 files via
ruff format with target-version=py314.
Update hacs.json minimum HA version to 2026.4.0, the first HA release
requiring Python 3.14.
Impact: Linter config now correctly handles Python 3.14 semantics.
Users need HA >= 2026.4 (Python 3.14) to use this integration.
Complete README rewrite focused on user value propositions instead
of exhaustive technical entity tables. Updated feature list to
reflect 100+ entities across sensors, binary sensors, switches,
and number platforms.
Key changes:
- "Why This Integration?" section with 5 value categories
- Compact entity highlights table replacing 6 detailed tables
- Added missing features: forecasts, trends, volatility, runtime
config (switches/numbers), period timing, interval pool
- Fixed broken /user/sensors link → /user/sensor-reference
- Reduced automation examples to 2 highlights + link
- Moved troubleshooting to doc links (FAQ + Troubleshooting)
- Updated service: → action: in YAML examples
Impact: README now serves as an inviting HACS storefront that
showcases all integration capabilities and encourages users to
try it, instead of overwhelming with technical details.
