hass.tibber_prices/docs/user/versioned_docs/version-v0.25.0b0/sensors.md

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Sensors

Note: This guide is under construction. For now, please refer to the main README for available sensors.

Tip: Many sensors have dynamic icons and colors! See the Dynamic Icons Guide and Dynamic Icon Colors Guide to enhance your dashboards.

Entity ID tip: <home_name> is a placeholder for your Tibber home display name in Home Assistant. Entity IDs are derived from the displayed name (localized), so the exact slug may differ. Example suffixes below use the English display names (en.json) as a baseline. You can find the real ID in Settings → Devices & Services → Entities (or Developer Tools → States).

Binary Sensors

Best Price Period & Peak Price Period

These binary sensors indicate when you're in a detected best or peak price period. See the Period Calculation Guide for a detailed explanation of how these periods are calculated and configured.

Quick overview:

• Best Price Period: Turns ON during periods with significantly lower prices than the daily average
• Peak Price Period: Turns ON during periods with significantly higher prices than the daily average

Both sensors include rich attributes with period details, intervals, relaxation status, and more.

Core Price Sensors

Average Price Sensors

The integration provides several sensors that calculate average electricity prices over different time windows. These sensors show a typical price value that represents the overall price level, helping you make informed decisions about when to use electricity.

Available Average Sensors

Sensor	Description	Time Window
Average Price Today	Typical price for current calendar day	00:00 - 23:59 today
Average Price Tomorrow	Typical price for next calendar day	00:00 - 23:59 tomorrow
Trailing Price Average	Typical price for last 24 hours	Rolling 24h backward
Leading Price Average	Typical price for next 24 hours	Rolling 24h forward
Current Hour Average	Smoothed price around current time	5 intervals (~75 min)
Next Hour Average	Smoothed price around next hour	5 intervals (~75 min)
Next N Hours Average	Future price forecast	1h, 2h, 3h, 4h, 5h, 6h, 8h, 12h

Configurable Display: Median vs Mean

All average sensors support two different calculation methods for the state value:

• Median (default): The "middle value" when all prices are sorted. Resistant to extreme price spikes, shows the typical price level you experienced.
• Arithmetic Mean: The mathematical average including all prices. Better for cost calculations but affected by extreme spikes.

Why two values matter:

# Example price data for one day:
# Prices: 10, 12, 13, 15, 80 ct/kWh (one extreme spike)
#
# Median = 13 ct/kWh    ← "Typical" price level (middle value)
# Mean = 26 ct/kWh      ← Mathematical average (affected by spike)

The median shows you what price level was typical during that period, while the mean shows the actual average cost if you consumed evenly throughout the period.

Configuring the Display

You can choose which value is displayed in the sensor state:

1. Go to Settings → Devices & Services → Tibber Prices
2. Click Configure on your home
3. Navigate to Step 6: Average Sensor Display Settings
4. Choose between:
   • Median (default) - Shows typical price level, resistant to spikes
   • Arithmetic Mean - Shows actual mathematical average

Important: Both values are always available as sensor attributes, regardless of your choice! This ensures your automations continue to work if you change the display setting.

Using Both Values in Automations

Both price_mean and price_median are always available as attributes:

# Example: Get both values regardless of display setting
sensor:
  - platform: template
    sensors:
      daily_price_analysis:
        friendly_name: "Daily Price Analysis"
        value_template: >
          {% set median = state_attr('sensor.<home_name>_price_today', 'price_median') %}
          {% set mean = state_attr('sensor.<home_name>_price_today', 'price_mean') %}
          {% set current = states('sensor.<home_name>_current_electricity_price') | float %}

          {% if current < median %}
            Below typical ({{ ((1 - current/median) * 100) | round(1) }}% cheaper)
          {% elif current < mean %}
            Typical price range
          {% else %}
            Above average ({{ ((current/mean - 1) * 100) | round(1) }}% more expensive)
          {% endif %}          

Practical Examples

Example 1: Smart dishwasher control

Run dishwasher only when price is significantly below the daily typical level:

automation:
  - alias: "Start Dishwasher When Cheap"
    trigger:
      - platform: state
        entity_id: binary_sensor.<home_name>_best_price_period
        to: "on"
    condition:
      # Only if current price is at least 20% below typical (median)
      - condition: template
        value_template: >
          {% set current = states('sensor.<home_name>_current_electricity_price') | float %}
          {% set median = state_attr('sensor.<home_name>_price_today', 'price_median') | float %}
          {{ current < (median * 0.8) }}          
    action:
      - service: switch.turn_on
        entity_id: switch.dishwasher

Example 2: Cost-aware heating control

Use mean for actual cost calculations:

automation:
  - alias: "Heating Budget Control"
    trigger:
      - platform: time
        at: "06:00:00"
    action:
      # Calculate expected daily heating cost
      - variables:
          mean_price: "{{ state_attr('sensor.<home_name>_price_today', 'price_mean') | float }}"
          heating_kwh_per_day: 15  # Estimated consumption
          daily_cost: "{{ (mean_price * heating_kwh_per_day / 100) | round(2) }}"
      - service: notify.mobile_app
        data:
          title: "Heating Cost Estimate"
          message: "Expected cost today: €{{ daily_cost }} (avg price: {{ mean_price }} ct/kWh)"

Example 3: Smart charging based on rolling average

Use trailing average to understand recent price trends:

automation:
  - alias: "EV Charging - Price Trend Based"
    trigger:
      - platform: state
        entity_id: sensor.ev_battery_level
    condition:
      # Start charging if current price < 90% of recent 24h average
      - condition: template
        value_template: >
          {% set current = states('sensor.<home_name>_current_electricity_price') | float %}
          {% set trailing_avg = state_attr('sensor.<home_name>_price_trailing_24h', 'price_median') | float %}
          {{ current < (trailing_avg * 0.9) }}          
      # And battery < 80%
      - condition: numeric_state
        entity_id: sensor.ev_battery_level
        below: 80
    action:
      - service: switch.turn_on
        entity_id: switch.ev_charger

Key Attributes

All average sensors provide these attributes:

Attribute	Description	Example
price_mean	Arithmetic mean (always available)	25.3 ct/kWh
price_median	Median value (always available)	22.1 ct/kWh
interval_count	Number of intervals included	96
timestamp	Reference time for calculation	2025-12-18T00:00:00+01:00

Note: The price_mean and price_median attributes are always present regardless of which value you configured for display. This ensures automation compatibility when changing the display setting.

When to Use Which Value

Use Median for:

• ✅ Comparing "typical" price levels across days
• ✅ Determining if current price is unusually high/low
• ✅ User-facing displays ("What was today like?")
• ✅ Volatility analysis (comparing typical vs extremes)

Use Mean for:

• ✅ Cost calculations and budgeting
• ✅ Energy cost estimations
• ✅ Comparing actual average costs between periods
• ✅ Financial planning and forecasting

Both values tell different stories:

• High median + much higher mean = Expensive spikes occurred
• Low median + higher mean = Generally cheap with occasional spikes
• Similar median and mean = Stable prices (low volatility)

Volatility Sensors

Volatility sensors help you understand how much electricity prices fluctuate over a given period. Instead of just looking at the absolute price, they measure the relative price variation, which is a great indicator of whether it's a good day for price-based energy optimization.

The calculation is based on the Coefficient of Variation (CV), a standardized statistical measure defined as:

CV = (Standard Deviation / aAithmetic Mean) * 100%

This results in a percentage that shows how much prices deviate from the average. A low CV means stable prices, while a high CV indicates significant price swings and thus, a high potential for saving money by shifting consumption.

The sensor's state can be low, moderate, high, or very_high, based on configurable thresholds.

Available Volatility Sensors

Sensor	Description	Time Window
Today's Price Volatility	Volatility for the current calendar day	00:00 - 23:59 today
Tomorrow's Price Volatility	Volatility for the next calendar day	00:00 - 23:59 tomorrow
Next 24h Price Volatility	Volatility for the next 24 hours from now	Rolling 24h forward
Today + Tomorrow Price Volatility	Volatility across both today and tomorrow	Up to 48 hours

Configuration

You can adjust the CV thresholds that determine the volatility level:

1. Go to Settings → Devices & Services → Tibber Prices.
2. Click Configure.
3. Go to the Price Volatility Thresholds step.

Default thresholds are:

• Moderate: 15%
• High: 30%
• Very High: 50%

Key Attributes

All volatility sensors provide these attributes:

Attribute	Description	Example
price_coefficient_variation_%	The calculated Coefficient of Variation	23.5
price_spread	The difference between the highest and lowest price	12.3
price_min	The lowest price in the period	10.2
price_max	The highest price in the period	22.5
price_mean	The arithmetic mean of all prices in the period	15.1
interval_count	Number of price intervals included in the calculation	96

Usage in Automations & Best Practices

You can use the volatility sensor to decide if a price-based optimization is worth it. For example, if your solar battery has conversion losses, you might only want to charge and discharge it on days with high volatility.

Best Practice: Use the price_volatility Attribute

For automations, it is strongly recommended to use the price_volatility attribute instead of the sensor's main state.

• Why? The main state of the sensor is translated into your Home Assistant language (e.g., "Hoch" in German). If you change your system language, automations based on this state will break. The price_volatility attribute is always in lowercase English ("low", "moderate", "high", "very_high") and therefore provides a stable, language-independent value.

Good Example (Robust Automation): This automation triggers only if the volatility is classified as high or very_high, respecting your central settings and working independently of the system language.

automation:
  - alias: "Enable battery optimization only on volatile days"
    trigger:
      - platform: template
        value_template: >
          {{ state_attr('sensor.<home_name>_today_s_price_volatility', 'price_volatility') in ['high', 'very_high'] }}          
    action:
      - service: input_boolean.turn_on
        entity_id: input_boolean.battery_optimization_enabled

---

Avoid Hard-Coding Numeric Thresholds

You might be tempted to use the numeric price_coefficient_variation_% attribute directly in your automations. This is not recommended.

• Why? The integration provides central configuration options for the volatility thresholds. By using the classified price_volatility attribute, your automations automatically adapt if you decide to change what you consider "high" volatility (e.g., changing the threshold from 30% to 35%). Hard-coding values means you would have to find and update them in every single automation.

Bad Example (Brittle Automation): This automation uses a hard-coded value. If you later change the "High" threshold in the integration's options to 35%, this automation will not respect that change and might trigger at the wrong time.

automation:
  - alias: "Brittle - Enable battery optimization"
    trigger:
      #
      # BAD: Avoid hard-coding numeric values
      #
      - platform: numeric_state
        entity_id: sensor.<home_name>_today_s_price_volatility
        attribute: price_coefficient_variation_%
        above: 30
    action:
      - service: input_boolean.turn_on
        entity_id: input_boolean.battery_optimization_enabled

By following the "Good Example", your automations become simpler, more readable, and much easier to maintain.

Rating Sensors

Coming soon...

Diagnostic Sensors

Chart Metadata

Entity ID: sensor.<home_name>_chart_metadata

✨ New Feature: This sensor provides dynamic chart configuration metadata for optimal visualization. Perfect for use with the get_apexcharts_yaml action!

This diagnostic sensor provides essential chart configuration values as sensor attributes, enabling dynamic Y-axis scaling and optimal chart appearance in rolling window modes.

Key Features:

• Dynamic Y-Axis Bounds: Automatically calculates optimal yaxis_min and yaxis_max for your price data
• Automatic Updates: Refreshes when price data changes (coordinator updates)
• Lightweight: Metadata-only mode (no data processing) for fast response
• State Indicator: Shows pending (initialization), ready (data available), or error (service call failed)

Attributes:

• timestamp: When the metadata was last fetched
• yaxis_min: Suggested minimum value for Y-axis (optimal scaling)
• yaxis_max: Suggested maximum value for Y-axis (optimal scaling)
• currency: Currency code (e.g., "EUR", "NOK")
• resolution: Interval duration in minutes (usually 15)
• error: Error message if service call failed

Usage:

The tibber_prices.get_apexcharts_yaml action automatically uses this sensor for dynamic Y-axis scaling in rolling_window and rolling_window_autozoom modes! No manual configuration needed - just enable the action's result with config-template-card and the sensor provides optimal Y-axis bounds automatically.

See the Chart Examples Guide for practical examples!

---

Chart Data Export

Entity ID: sensor.<home_name>_chart_data_export Default State: Disabled (must be manually enabled)

⚠️ Legacy Feature: This sensor is maintained for backward compatibility. For new integrations, use the tibber_prices.get_chartdata service instead, which offers more flexibility and better performance.

This diagnostic sensor provides cached chart-friendly price data that can be consumed by chart cards (ApexCharts, custom cards, etc.).

Key Features:

• Configurable via Options Flow: Service parameters can be configured through the integration's options menu (Step 7 of 7)
• Automatic Updates: Data refreshes on coordinator updates (every 15 minutes)
• Attribute-Based Output: Chart data is stored in sensor attributes for easy access
• State Indicator: Shows pending (before first call), ready (data available), or error (service call failed)

Important Notes:

• ⚠️ Disabled by default - must be manually enabled in entity settings
• ⚠️ Consider using the service instead for better control and flexibility
• ⚠️ Configuration updates require HA restart

Attributes:

The sensor exposes chart data with metadata in attributes:

• timestamp: When the data was last fetched
• error: Error message if service call failed
• data (or custom name): Array of price data points in configured format

Configuration:

To configure the sensor's output format:

1. Go to Settings → Devices & Services → Tibber Prices
2. Click Configure on your Tibber home
3. Navigate through the options wizard to Step 7: Chart Data Export Settings
4. Configure output format, filters, field names, and other options
5. Save and restart Home Assistant

Available Settings:

See the tibber_prices.get_chartdata service documentation below for a complete list of available parameters. All service parameters can be configured through the options flow.

Example Usage:

# ApexCharts card consuming the sensor
type: custom:apexcharts-card
series:
    - entity: sensor.<home_name>_chart_data_export
      data_generator: |
          return entity.attributes.data;          

Migration Path:

If you're currently using this sensor, consider migrating to the service:

# Old approach (sensor)
- service: apexcharts_card.update
  data:
      entity: sensor.<home_name>_chart_data_export

# New approach (service)
- service: tibber_prices.get_chartdata
  data:
      entry_id: YOUR_ENTRY_ID
      day: ["today", "tomorrow"]
      output_format: array_of_objects
  response_variable: chart_data