Combine the state of several sensors

The combination sensor platform allows you to combine the state of several sensors into one. To use this sensor, specify the combination type and add your source sensors.

The unit_of_measurement, device_class, entity_category, icon, and accuracy_decimals properties are by default inherited from the first sensor. state_class is explicitly not inherited, because total_increasing states could still decrease when multiple sensors are used for several of the combination types.

The source sensor states can be combined in several ways:

KALMAN filter: This type filters one or several sensors into one with a reduced error. If using a single sensor as data source, it acts like a sensor-filter-exponential_moving_average filter. With multiple sensors, it combines their values based on their respective standard deviation.

# Example configuration entry
sensor:
  - platform: combination
    type: kalman
    name: "Temperature"
    process_std_dev: 0.001
    sources:
      - source: temperature_sensor_1
        error: 1.0
      - source: temperature_sensor_2
        error: !lambda |-
          return 0.5 + std::abs(x - 25) * 0.023

LINEAR combination: This type sums all source sensors after multiplying each by a configured coeffecient.

# Example configuration entry
sensor:
  - platform: combination
    type: linear
    name: "Balance Power"
    sources:
      - source: total_power
        coeffecient: 1.0
      - source: circuit_1_power
        coeffecient: -1.0

MAXIMUM, MEAN, MEDIAN, MINIMUM, MOST_RECENTLY_UPDATED, RANGE, SUM combinations: These types compute the specified combination among all source sensor states.

# Example configuration entry
sensor:
  - platform: combination
    type: median
    name: "Median Temperature"
    sources:
      - source: temperature_sensor_1
      - source: temperature_sensor_2
      - source: temperature_sensor_3

Configuration variables

type (Required, enum): Combination statistic type, should be one of KALMAN, LINEAR, MAXIMUM, MEAN, MEDIAN, MINIMUM, MOST_RECENTLY_UPDATED, RANGE, or SUM.
sources (Required, list): A list of sensors to use as source.
- source (Required, ID of a Sensor Component): The sensor id that is used as sample source.
- error (Required, only for KALMAN type, float, templatable): The standard deviation of the sensor’s measurements. This works like the process_std_dev parameter, with low values marking accurate data. If implemented as a template, the measurement is in parameter x.
- coeffecient (Required, only for LINEAR type, float, templatable): The coeffecient to multiply the sensor’s state by before summing all source sensor states. If implemented as a template, the measurement is in parameter x.
process_std_dev (Required, only for KALMAN type, float): The standard deviation of the measurement’s change per second (e.g. 1/3600 = 0.000277 if the temperature usually changes at most by one Kelvin per hour). A low value here will place high importance on the current state and be slow to respond to changes in the measured samples. A high value will update faster, but also be more noisy.
std_dev (Optional, only for KALMAN type, Sensor): A sensor that publishes the current standard deviation of the state with each update.
All other options from Sensor.