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.
