mirror of https://github.com/jpawlowski/hass.tibber_prices.git synced 2026-03-29 21:03:40 +00:00

History

Julian Pawlowski 383b495545 Feature/adaptive defaults (#22 ) * feat(period-calc): adaptive defaults + remove volatility filter Major improvements to period calculation with smarter defaults and simplified configuration: Adaptive Defaults: - ENABLE_MIN_PERIODS: true (was false) - Always try to find periods - MIN_PERIODS target: 2 periods/day (ensures coverage) - BEST_PRICE_MAX_LEVEL: "cheap" (was "any") - Prefer genuinely cheap - PEAK_PRICE_MIN_LEVEL: "expensive" (was "any") - Prefer genuinely expensive - GAP_TOLERANCE: 1 (was 0) - Allow 1-level deviations in sequences - MIN_DISTANCE_FROM_AVG: 5% (was 2%) - Ensure significance - PEAK_PRICE_MIN_PERIOD_LENGTH: 30min (was 60min) - More responsive - PEAK_PRICE_FLEX: -20% (was -15%) - Better peak detection Volatility Filter Removal: - Removed CONF_BEST_PRICE_MIN_VOLATILITY from const.py - Removed CONF_PEAK_PRICE_MIN_VOLATILITY from const.py - Removed volatility filter UI controls from config_flow.py - Removed filter_periods_by_volatility() calls from coordinator.py - Updated all 5 translations (de, en, nb, nl, sv) Period Calculation Logic: - Level filter now integrated into _build_periods() (applied during interval qualification, not as post-filter) - Gap tolerance implemented via _check_level_with_gap_tolerance() - Short periods (<1.5h) use strict filtering (no gap tolerance) - Relaxation now passes level_filter + gap_count directly to PeriodConfig - show_periods check skipped when relaxation enabled (relaxation tries "any" as fallback) Documentation: - Complete rewrite of docs/user/period-calculation.md: * Visual examples with timelines * Step-by-step explanation of 4-step process * Configuration scenarios (5 common use cases) * Troubleshooting section with specific fixes * Advanced topics (per-day independence, early stop, etc.) - Updated README.md: "volatility" → "distance from average" Impact: Periods now reliably appear on most days with meaningful quality filters. Users get warned about expensive periods and notified about cheap opportunities without manual tuning. Relaxation ensures coverage while keeping filters as strict as possible. Breaking change: Volatility filter removed (was never a critical feature, often confused users). Existing configs continue to work (removed keys are simply ignored). * feat(periods): modularize period_utils and add statistical outlier filtering Refactored monolithic period_utils.py (1800 lines) into focused modules for better maintainability and added advanced outlier filtering with smart impact tracking. Modular structure: - types.py: Type definitions and constants (89 lines) - level_filtering.py: Level filtering with gap tolerance (121 lines) - period_building.py: Period construction from intervals (238 lines) - period_statistics.py: Statistics and summaries (318 lines) - period_merging.py: Overlap resolution (382 lines) - relaxation.py: Per-day relaxation strategy (547 lines) - core.py: Main API orchestration (251 lines) - outlier_filtering.py: Statistical spike detection (294 lines) - __init__.py: Public API exports (62 lines) New statistical outlier filtering: - Linear regression for trend-based spike detection - 2 standard deviation confidence intervals (95%) - Symmetry checking to preserve legitimate price shifts - Enhanced zigzag detection with relative volatility (catches clusters) - Replaces simple average smoothing with trend-based predictions Smart impact tracking: - Tests if original price would have passed criteria - Only counts smoothed intervals that actually changed period formation - Tracks level gap tolerance usage separately - Both attributes only appear when > 0 (clean UI) New period attributes: - period_interval_smoothed_count: Intervals kept via outlier smoothing - period_interval_level_gap_count: Intervals kept via gap tolerance Impact: Statistical outlier filtering prevents isolated price spikes from breaking continuous periods while preserving data integrity. All statistics use original prices. Smart tracking shows only meaningful interventions, making it clear when tolerance mechanisms actually influenced results. Backwards compatible: All public APIs re-exported from period_utils package. * Update docs/user/period-calculation.md Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update custom_components/tibber_prices/const.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update custom_components/tibber_prices/coordinator.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update custom_components/tibber_prices/const.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * docs(periods): fix corrupted period-calculation.md and add outlier filtering documentation Completely rewrote period-calculation.md after severe corruption (massive text duplication throughout the file made it 2489 lines). Changes: - Fixed formatting: Removed all duplicate text and headers - Reduced file size: 2594 lines down to 516 lines (clean, readable structure) - Added section 5: "Statistical Outlier Filtering (NEW)" explaining: - Linear regression-based spike detection (95% confidence intervals) - Symmetry checking to preserve legitimate price shifts - Enhanced zigzag detection with relative volatility - Data integrity guarantees (original prices always used) - New period attributes: period_interval_smoothed_count - Added troubleshooting: "Price spikes breaking periods" section - Added technical details: Algorithm constants and implementation notes Impact: Users can now understand how outlier filtering prevents isolated price spikes from breaking continuous periods. Documentation is readable again with no duplicate content. * fix(const): improve clarity in comments regarding period lengths for price alerts * docs(periods): improve formatting and clarity in period-calculation.md * Initial plan * refactor: convert flexibility_pct to ratio once at function entry Co-authored-by: jpawlowski <75446+jpawlowski@users.noreply.github.com> * Update custom_components/tibber_prices/const.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update custom_components/tibber_prices/period_utils/period_building.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update custom_components/tibber_prices/period_utils/relaxation.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Julian Pawlowski <jpawlowski@users.noreply.github.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>		2025-11-13 23:51:29 +01:00
..
automation-examples.md	docs: restructure documentation and add AI development disclosure	2025-11-09 14:25:27 +00:00
configuration.md	docs: restructure documentation and add AI development disclosure	2025-11-09 14:25:27 +00:00
installation.md	docs: restructure documentation and add AI development disclosure	2025-11-09 14:25:27 +00:00
period-calculation.md	Feature/adaptive defaults (#22 )	2025-11-13 23:51:29 +01:00
README.md	feat(docs): enhance period calculation documentation and add detailed guides for best/peak price periods	2025-11-10 11:54:06 +00:00
sensors.md	feat(docs): enhance period calculation documentation and add detailed guides for best/peak price periods	2025-11-10 11:54:06 +00:00
services.md	docs: restructure documentation and add AI development disclosure	2025-11-09 14:25:27 +00:00
troubleshooting.md	docs: restructure documentation and add AI development disclosure	2025-11-09 14:25:27 +00:00

README.md

User Documentation

Welcome to Tibber Prices! This integration provides enhanced electricity price data from Tibber with quarter-hourly precision, statistical analysis, and intelligent ratings.

📚 Documentation

Installation - How to install via HACS and configure the integration
Configuration - Setting up your Tibber API token and price thresholds
Period Calculation - How Best/Peak Price periods are calculated and configured
Sensors - Available sensors, their states, and attributes
Services - Custom services and how to use them
Automation Examples - Ready-to-use automation recipes
Troubleshooting - Common issues and solutions

🚀 Quick Start

Install via HACS (add as custom repository)
Add Integration in Home Assistant → Settings → Devices & Services
Enter Tibber API Token (get yours at developer.tibber.com)
Configure Price Thresholds (optional, defaults work for most users)
Start Using Sensors in automations, dashboards, and scripts!

✨ Key Features

Quarter-hourly precision - 15-minute intervals for accurate price tracking
Statistical analysis - Trailing/leading 24h averages for context
Price ratings - LOW/NORMAL/HIGH classification based on your thresholds
Best/Peak hour detection - Automatic detection of cheapest/peak periods with configurable filters (learn how)
ApexCharts integration - Custom services for beautiful price charts
Multi-currency support - EUR, NOK, SEK with proper minor units (ct, øre, öre)

🔗 Useful Links

🤝 Need Help?

Check the Troubleshooting Guide
Search existing issues
Open a new issue if needed

Note: These guides are for end users. If you want to contribute to development, see the Developer Documentation.