Home Assistant Home Assistant

Derivative

The derivative platform creates a sensor that estimates the derivative of the values provided by a source sensor. Derivative sensors are updated upon changes of the source.

Configuration

To enable Derivative Sensor in your installation, add the following to your configuration.yaml file:

# Example configuration.yaml entry
sensor:
  - platform: derivative
    source: sensor.current_speed

Configuration Variables

Looking for your configuration file?
source string Required

The entity ID of the sensor providing numeric readings

name string (Optional, default: source entity ID meter)

Name to use in the frontend.

round integer (Optional, default: 3)

Round the calculated derivative value to at most N decimal places.

unit_prefix string (Optional, default: None)

Metric unit to prefix the derivative result (Wikipedia). Available symbols are “n” (1e-9), “µ” (1e-6), “m” (1e-3), “k” (1e3), “M” (1e6), “G” (1e9), “T” (1e12).

unit_time string (Optional, default: h)

SI unit of time of the derivative. Available units are s, min, h, d. If this parameter is set, the attribute unit_of_measurement will be set like x/y where x is the unit of the sensor given via the source parameter and y is the value given here.

unit string (Optional)

Unit of Measurement to be used for the derivative. This will overwrite the automatically set unit_of_measurement as explained above.

time_window time (Optional, default: 0)

The time window in which to calculate the derivative. This is useful for sensor that output discrete values. By default the derivative is calculated between two consecutive updates.

Temperature example

For example, you have a temperature sensor sensor.temperature that outputs a value every few seconds, but rounds to the nearest half number. That means that two consecutive output values might be the same (so the derivative is Δy/Δx=0 because Δy=0 !) However, the temperature might actually be changing over time. In order to capture this, you should use a time_window, such that immediate jumps don’t result in high derivatives and that after the next sensor update, the derivatives doesn’t vanish to zero. An example configuration that uses time_window is

sensor:
  - platform: derivative
    source: sensor.temperature
    name: Temperature change per hour
    round: 1
    unit_time: h # the resulting "unit_of_measurement" will be °C/h if the sensor.temperate has set °C as it's unit
    time_window: "00:30:00"  # we look at the change over the last half hour