When include_current_interval=False, the search_start was set to the raw
now timestamp (e.g. 14:47:00.167996). The interval pool index iterates in
15-minute steps from that exact value, so keys like 14:47:00, 15:02:00, …
never matched any cached interval (which are always on :00/:15/:30/:45
boundaries), causing no data to be found and returning no_data_in_range.
Fix: replace `now` with `ceil_to_next_quarter_hour(now)` which is always
`floor(now) + 15min`. This skips the interval that is currently in progress
(14:30–14:45 interval) and starts at the first interval that hasn't begun
yet (15:00), consistent with the intent of include_current_interval=False.
Fixes#190
User-Impact: find_cheapest_block/find_most_expensive_block/find_cheapest_hours
with include_current_interval=false and search_scope=next_24h (or no scope)
no longer returns no_data_in_range when data is available.
Agent-Logs-Url: https://github.com/jpawlowski/hass.tibber_prices/sessions/0ea804f9-7c98-4e5a-b40f-469954fa9daa
Co-authored-by: jpawlowski <75446+jpawlowski@users.noreply.github.com>
check_min_distance_from_avg() computed the distance threshold as
range_avg * (1 ± ratio). Tibber prices can go negative during grid
oversupply, and multiplying a negative range_avg directly flips the
intended direction: e.g. avg * 1.05 makes a negative average MORE
negative (i.e. cheaper), which is the wrong direction for a
"most expensive" threshold check, and analogously wrong for
"cheapest" checks.
Compute the threshold as range_avg ± abs(range_avg) * ratio instead,
matching the sign-safe normalization pattern already used for
min_distance_from_avg in the period system
(coordinator/period_handlers/level_filtering.py). Behavior for
positive averages (the common case) is unchanged.
Used by find_cheapest_block, find_cheapest_hours, and plan_charging.
Impact: min_distance_from_avg now correctly filters cheapest/most
expensive windows during negative-price periods instead of silently
accepting windows in the wrong direction relative to the search range
average.
Add a track_degraded parameter to IntervalPool.get_intervals so service calls
(track_degraded=False) never flip the coordinator's sensor-health flags or use
the covers-current-interval cache fallback; authentication errors still
propagate. All price-fetching services now go through
async_fetch_service_intervals and return a structured response with a
top-level success flag: success=false with reason="price_data_unavailable" on
a real API outage, and success=true (possibly empty) otherwise.
Impact: Action calls (get_price, find_cheapest_*, plan_charging) always return
the expected shape, never impair sensors during a Tibber outage, and let
automations distinguish an outage from "no data yet".
Add `include_current_interval` parameter to `find_cheapest_block` and
`find_cheapest_schedule` services, controlling whether the currently
active price interval can be the start of the selected window.
Add power-profile weighting to `find_cheapest_contiguous_window`: accepts
an optional `power_profile` list that weights each interval's price by
relative power draw (e.g. heat-up phase heavier than steady state). Without
a profile the behaviour is unchanged (uniform weighting).
Extend search-range tests and add price-window unit tests covering weighted
and unweighted scenarios, edge cases, and sequential scheduling interactions.
Update scheduling-actions documentation with parameter and profile examples.
Impact: Users can now model appliances with non-uniform power draw (e.g. heat
pumps, washing machines) to find truly cheapest windows based on actual energy
cost rather than average price.
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
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.
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.
Add dynamic rolling window mode to get_chartdata and get_apexcharts_yaml
services that automatically adapts to data availability.
When 'day' parameter is omitted, services return 48-hour window:
- With tomorrow data (after ~13:00): today + tomorrow
- Without tomorrow data: yesterday + today
Changes:
- Implement rolling window logic in get_chartdata using has_tomorrow_data()
- Generate config-template-card wrapper in get_apexcharts_yaml for dynamic
ApexCharts span.offset based on tomorrow_data_available binary sensor
- Update service descriptions in services.yaml
- Add rolling window descriptions to all translations (de, en, nb, nl, sv)
- Document rolling window mode in docs/user/services.md
- Add ApexCharts examples with prerequisites in docs/user/automation-examples.md
BREAKING CHANGE: get_apexcharts_yaml rolling window mode requires
config-template-card in addition to apexcharts-card for dynamic offset
calculation.
Impact: Users can create auto-adapting 48h price charts without manual day
selection. Fixed day views (day: today/yesterday/tomorrow) still work with
apexcharts-card only.
