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Period Calculation

Learn how Best Price and Peak Price periods work, and how to configure them for your needs.

Table of Contents

Quick Start

What Are Price Periods?

The integration finds time windows when electricity is especially cheap (Best Price) or expensive (Peak Price):

  • Best Price Periods 🟢 - When to run your dishwasher, charge your EV, or heat water
  • Peak Price Periods 🔴 - When to reduce consumption or defer non-essential loads

Default Behavior

Out of the box, the integration:

  1. Best Price: Finds cheapest 1-hour+ windows that are at least 2% below the daily average
  2. Peak Price: Finds most expensive 1-hour+ windows that are at least 2% above the daily average
  3. Relaxation: Automatically loosens filters if not enough periods are found

Most users don't need to change anything! The defaults work well for typical use cases.

Example Timeline

00:00 ████████████████ Best Price Period (cheap prices)
04:00 ░░░░░░░░░░░░░░░░ Normal
08:00 ████████████████ Peak Price Period (expensive prices)
12:00 ░░░░░░░░░░░░░░░░ Normal
16:00 ████████████████ Peak Price Period (expensive prices)
20:00 ████████████████ Best Price Period (cheap prices)

How It Works

The Basic Idea

Each day, the integration analyzes all 96 quarter-hourly price intervals and identifies continuous time ranges that meet specific criteria.

Think of it like this:

  1. Find potential windows - Times close to the daily MIN (Best Price) or MAX (Peak Price)
  2. Filter by quality - Ensure they're meaningfully different from average
  3. Check duration - Must be long enough to be useful
  4. Apply preferences - Optional: only show stable prices, avoid mediocre times

Step-by-Step Process

1. Define the Search Range (Flexibility)

Best Price: How much MORE than the daily minimum can a price be?

Daily MIN: 20 ct/kWh
Flexibility: 15% (default)
→ Search for times ≤ 23 ct/kWh (20 + 15%)

Peak Price: How much LESS than the daily maximum can a price be?

Daily MAX: 40 ct/kWh
Flexibility: -15% (default)
→ Search for times ≥ 34 ct/kWh (40 - 15%)

Why flexibility? Prices rarely stay at exactly MIN/MAX. Flexibility lets you capture realistic time windows.

2. Ensure Quality (Distance from Average)

Periods must be meaningfully different from the daily average:

Daily AVG: 30 ct/kWh
Minimum distance: 2% (default)

Best Price: Must be ≤ 29.4 ct/kWh (30 - 2%)
Peak Price: Must be ≥ 30.6 ct/kWh (30 + 2%)

Why? This prevents marking mediocre times as "best" just because they're slightly below average.

3. Check Duration

Periods must be long enough to be practical:

Default: 60 minutes minimum

45-minute period → Discarded
90-minute period → Kept ✓

4. Apply Optional Filters

You can optionally require:

  • Stable prices (volatility filter) - "Only show if price doesn't fluctuate much"
  • Absolute quality (level filter) - "Only show if prices are CHEAP/EXPENSIVE (not just below/above average)"

5. Statistical Outlier Filtering

Before period identification, price spikes are automatically detected and smoothed:

Raw prices:    18, 19, 35, 20, 19 ct   ← 35 ct is an isolated spike
Smoothed:      18, 19, 19, 20, 19 ct   ← Spike replaced with trend prediction

Result: Continuous period 00:00-01:15 instead of split periods

How it works:

  • Linear regression predicts expected price based on surrounding trend
  • 95% confidence intervals (2 standard deviations) define spike tolerance
  • Symmetry checking preserves legitimate price shifts (morning/evening peaks)
  • Enhanced zigzag detection catches spike clusters without multiple passes

Data integrity:

  • Original prices always preserved for statistics (min/max/avg show real values)
  • Smoothing only affects period formation (which intervals qualify for periods)
  • Attributes show when smoothing was impactful: period_interval_smoothed_count

Example log output:

DEBUG: [2025-11-11T14:30:00+01:00] Outlier detected: 35.2 ct
DEBUG:   Residual: 14.5 ct > tolerance: 4.8 ct (2×2.4 std dev)
DEBUG:   Trend slope: 0.3 ct/interval (gradual increase)
DEBUG:   Smoothed to: 20.7 ct (trend prediction)

Visual Example

Timeline for a typical day:

Hour:  00  01  02  03  04  05  06  07  08  09  10  11  12  13  14  15  16  17  18  19  20  21  22  23
Price: 18  19  20  28  29  30  35  34  33  32  30  28  25  24  26  28  30  32  31  22  21  20  19  18

Daily MIN: 18 ct | Daily MAX: 35 ct | Daily AVG: 26 ct

Best Price (15% flex = ≤20.7 ct):
       ████████                                                                        ████████████████
       00:00-03:00 (3h)                                                               19:00-24:00 (5h)

Peak Price (-15% flex = ≥29.75 ct):
                              ████████████████████████
                              06:00-11:00 (5h)

Configuration Guide

Basic Settings

Flexibility

What: How far from MIN/MAX to search for periods Default: 15% (Best Price), -15% (Peak Price) Range: 0-100%

best_price_flex: 15 # Can be up to 15% more expensive than daily MIN
peak_price_flex: -15 # Can be up to 15% less expensive than daily MAX

When to adjust:

  • Increase (20-25%) → Find more/longer periods
  • Decrease (5-10%) → Find only the very best/worst times

Minimum Period Length

What: How long a period must be to show it Default: 60 minutes Range: 15-240 minutes

best_price_min_period_length: 60
peak_price_min_period_length: 60

When to adjust:

  • Increase (90-120 min) → Only show longer periods (e.g., for heat pump cycles)
  • Decrease (30-45 min) → Show shorter windows (e.g., for quick tasks)

Distance from Average

What: How much better than average a period must be Default: 2% Range: 0-20%

best_price_min_distance_from_avg: 2
peak_price_min_distance_from_avg: 2

When to adjust:

  • Increase (5-10%) → Only show clearly better times
  • Decrease (0-1%) → Show any time below/above average

Optional Filters

Volatility Filter (Price Stability)

What: Only show periods with stable prices (low fluctuation) Default: low (disabled) Options: low | moderate | high | very_high

best_price_min_volatility: low        # Show all periods
best_price_min_volatility: moderate   # Only show if price doesn't swing >5 ct

Use case: "I want predictable prices during the period"

Level Filter (Absolute Quality)

What: Only show periods with CHEAP/EXPENSIVE intervals (not just below/above average) Default: any (disabled) Options: any | cheap | very_cheap (Best Price) | expensive | very_expensive (Peak Price)

best_price_max_level: any      # Show any period below average
best_price_max_level: cheap    # Only show if at least one interval is CHEAP

Use case: "Only notify me when prices are objectively cheap/expensive"

Gap Tolerance (for Level Filter)

What: Allow some "mediocre" intervals within an otherwise good period Default: 0 (strict) Range: 0-10

best_price_max_level: cheap
best_price_max_level_gap_count: 2 # Allow up to 2 NORMAL intervals per period

Use case: "Don't split periods just because one interval isn't perfectly CHEAP"

Understanding Relaxation

What Is Relaxation?

Sometimes, strict filters find too few periods (or none). Relaxation automatically loosens filters until a minimum number of periods is found.

How to Enable

enable_min_periods_best: true
min_periods_best: 2 # Try to find at least 2 periods per day
relaxation_step_best: 35 # Increase flex by 35% per step (e.g., 15% → 20.25% → 27.3%)

How It Works (Smart 4×4 Matrix)

Relaxation uses a 4×4 matrix approach - trying 4 flexibility levels with 4 different filter combinations (16 attempts total per day):

Phase Matrix

For each day, the system tries:

4 Flexibility Levels:

  1. Original (e.g., 15%)
  2. +35% step (e.g., 20.25%)
  3. +35% step (e.g., 27.3%)
  4. +35% step (e.g., 36.9%)

4 Filter Combinations (per flexibility level):

  1. Original filters (your configured volatility + level)
  2. Remove volatility filter (keep level filter)
  3. Remove level filter (keep volatility filter)
  4. Remove both filters

Example progression:

Flex 15% + Original filters → Not enough periods
Flex 15% + Volatility=any   → Not enough periods
Flex 15% + Level=any        → Not enough periods
Flex 15% + All filters off  → Not enough periods
Flex 20.25% + Original      → SUCCESS! Found 2 periods ✓
(stops here - no need to try more)

Per-Day Independence

Critical: Each day relaxes independently:

Day 1: Finds 2 periods with flex 15% (original) → No relaxation needed
Day 2: Needs flex 27.3% + level=any → Uses relaxed settings
Day 3: Finds 2 periods with flex 15% (original) → No relaxation needed

Why? Price patterns vary daily. Some days have clear cheap/expensive windows (strict filters work), others don't (relaxation needed).

Common Scenarios

Scenario 1: Simple Best Price (Default)

Goal: Find the cheapest time each day to run dishwasher

Configuration:

# Use defaults - no configuration needed!
best_price_flex: 15 # (default)
best_price_min_period_length: 60 # (default)
best_price_min_distance_from_avg: 2 # (default)

What you get:

  • 1-3 periods per day with prices ≤ MIN + 15%
  • Each period at least 1 hour long
  • All periods at least 2% cheaper than daily average

Automation example:

automation:
    - trigger:
          - platform: state
            entity_id: binary_sensor.tibber_home_best_price_period
            to: "on"
      action:
          - service: switch.turn_on
            target:
                entity_id: switch.dishwasher

Troubleshooting

No Periods Found

Symptom: binary_sensor.tibber_home_best_price_period never turns "on"

Possible causes:

  1. Filters too strict

    # Try:
best_price_flex: 20 # Increase from default 15%
best_price_min_distance_from_avg: 1 # Reduce from default 2%

  2. Period length too long

    # Try:
best_price_min_period_length: 45 # Reduce from default 60 minutes

  3. Flat price curve (all prices very similar)

    • Enable relaxation to ensure at least some periods
    enable_min_periods_best: true
min_periods_best: 1

Periods Split Into Small Pieces

Symptom: Many short periods instead of one long period

Possible causes:

  1. Level filter too strict

    # One "NORMAL" interval splits an otherwise good period
# Solution: Use gap tolerance
best_price_max_level: cheap
best_price_max_level_gap_count: 2 # Allow 2 NORMAL intervals

  2. Flexibility too tight

    # One interval just outside flex range splits the period
# Solution: Increase flexibility
best_price_flex: 20 # Increase from 15%

  3. Price spikes breaking periods

    • Statistical outlier filtering should handle this automatically
    • Check logs for smoothing activity:
    DEBUG: [2025-11-11T14:30:00+01:00] Outlier detected: 35.2 ct
DEBUG:   Smoothed to: 20.7 ct (trend prediction)
    • If smoothing isn't working as expected, check:
      • Is spike truly isolated? (3+ similar prices in a row won't be smoothed)
      • Is it a legitimate price shift? (symmetry check preserves morning/evening peaks)

Understanding Sensor Attributes

Check period details:

# Entity: binary_sensor.tibber_home_best_price_period

# Attributes when "on":
start: "2025-11-11T02:00:00+01:00"
end: "2025-11-11T05:00:00+01:00"
duration_minutes: 180
rating_level: "LOW" # All intervals are LOW price
price_avg: 18.5 # Average price in this period
relaxation_active: true # This day used relaxation
relaxation_level: "price_diff_20.25%+level_any" # Found at flex 20.25%, level filter removed
period_interval_smoothed_count: 2 # 2 outliers were smoothed (only if >0)
period_interval_level_gap_count: 1 # 1 interval kept via gap tolerance (only if >0)

Advanced Topics

For advanced configuration patterns and technical deep-dive, see:

Quick Reference

Configuration Parameters:

Parameter Default Range Purpose
best_price_flex 15% 0-100% Search range from daily MIN
best_price_min_period_length 60 min 15-240 Minimum duration
best_price_min_distance_from_avg 2% 0-20% Quality threshold
best_price_min_volatility low low/mod/high/vhigh Stability filter
best_price_max_level any any/cheap/vcheap Absolute quality
best_price_max_level_gap_count 0 0-10 Gap tolerance
enable_min_periods_best false true/false Enable relaxation
min_periods_best - 1-10 Target periods per day
relaxation_step_best - 5-100% Relaxation increment

Peak Price: Same parameters with peak_price_* prefix (defaults: flex=-15%, same otherwise)

Price Levels Reference

The Tibber API provides price levels for each 15-minute interval:

Levels (based on trailing 24h average):

  • VERY_CHEAP - Significantly below average
  • CHEAP - Below average
  • NORMAL - Around average
  • EXPENSIVE - Above average
  • VERY_EXPENSIVE - Significantly above average

Outlier Filtering Technical Details

Algorithm:

  1. Linear regression: Predicts expected price based on surrounding trend
  2. Confidence intervals: 2 standard deviations (95% confidence)
  3. Symmetry check: Rejects asymmetric outliers (1.5 std dev threshold)
  4. Enhanced zigzag detection: Catches spike clusters with relative volatility (2.0× threshold)

Constants:

  • CONFIDENCE_LEVEL: 2.0 (95% confidence)
  • SYMMETRY_THRESHOLD: 1.5 std dev
  • RELATIVE_VOLATILITY_THRESHOLD: 2.0
  • MIN_CONTEXT_SIZE: 3 intervals minimum

Data integrity:

  • Smoothed intervals stored with _original_price field
  • All statistics (min/max/avg) use original prices
  • Period attributes show impact: period_interval_smoothed_count
  • Smart counting: Only counts smoothing that actually changed period formation

Last updated: November 12, 2025 Integration version: 2.0+