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.
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.
_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 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.
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.
Period data in array_of_arrays format now generates proper segment structure
for stepline charts. Each period produces 2-3 data points depending on
insert_nulls parameter:
1. Start time with price (begin period)
2. End time with price (hold price level)
3. End time with NULL (terminate segment, only if insert_nulls='segments'/'all')
This enables ApexCharts to correctly display periods as continuous blocks with
clean gaps between them. Previously only start point was generated, causing
periods to render as single points instead of continuous segments.
Changes:
- formatters.py: Updated get_period_data() to generate 2-3 points per period
- formatters.py: Added insert_nulls parameter to control NULL termination
- get_chartdata.py: Pass insert_nulls parameter to get_period_data()
- get_apexcharts_yaml.py: Set insert_nulls='segments' for period overlay
- get_apexcharts_yaml.py: Preserve NULL values in data_generator mapping
- get_apexcharts_yaml.py: Store original price for potential tooltip access
- tests: Added comprehensive period data format tests
Impact: Best price and peak price period overlays now display correctly as
continuous blocks with proper segment separation in ApexCharts cards.
Simplifies the connect_segments implementation to use a unified bridge-point
approach for all price transitions (up/down/same). Previously used
direction-dependent logic (hold vs connect points) which was unnecessarily
complex.
Changes:
- get_chartdata.py: Bridge points now always use next interval's price at
boundary timestamp, creating smooth visual connection between segments
- get_chartdata.py: Trailing NULL removal now conditional on insert_nulls mode
('segments' removes for header fix, 'all' preserves intentional gaps)
- get_apexcharts_yaml.py: Enable connect_segments by default, activate
show_states for header min/max display
- get_apexcharts_yaml.py: Remove extrema series (not compatible with
data_generator approach - ApexCharts requires entity time-series data)
- tests: Move test_connect_segments.py to tests/services/ to mirror source
structure
Impact: ApexCharts cards now show clean visual connections between price level
segments with proper header statistics display. Trailing NULLs no longer cause
"N/A" in headers for filtered data. Test organization improved for
maintainability.
