This integrations provides the Riemann sum of the values provided by a source sensor. The Riemann sum is an approximation of an integral by a finite sum.
The integration sensors are updated upon changes of the source. Fast sampling source sensors provide more accurate results. In this implementation, the default is the Trapezoidal method, but Left and Right methods can optionally be used.
To add the Integration - Riemann sum integral integration to your Home Assistant instance, use this My button:
If the above My button doesn’t work, you can also perform the following steps manually:
Browse to your Home Assistant instance.
At the top of the screen, select the tab: Helpers.
In the bottom right corner, select the Create helper button.
From the list, select Integration - Riemann sum integral.
Follow the instructions on screen to complete the setup.
Alternatively, this integration can be configured and set up manually via YAML
as well. To enable the Integration sensor in your installation, add the
following to your
# Example configuration.yaml entry
- platform: integration
An ID that uniquely identifies the integration sensor. Set this to a unique value to allow customization through the UI.
Round the calculated integration value to at most N decimal places.
Metric unit to prefix the integration result. Available units are
SI unit of time to integrate over. Available units are
In case you expect that your source sensor will provide several subsequent values that are equal, you should opt for the
left method to get accurate readings.
The unit of
source together with
unit_time is used to generate a unit for the integral product (e.g. a source in
W with prefix
k and time
h would result in
kWh). Note that
unit_time are also relevant to the Riemann sum calculation.
Riemann Sum is a approximation of an integral by a finite sum and is therefore intrinsically inaccurate, nonetheless, depending on the method used, values can be more or less accurate.
Regardless of the method used the integration will be more accurate if the source updates more often. If your source is not updated, neither will the Riemann Sum sensor, as all this integration does is calculate the next step in the event of a source update.
trapezoidal method follows the Trapezoidal rule. This method is the most accurate of the currently implemented methods, if the source updates often, since it better fits the curve of the intrinsic source.
left method follows the Left rule. The method underestimates the intrinsic source, but is extremely accurate at estimating rectangular functions which are very stable for long periods of time and change very rapidly (e.g. such as the power function of a resistive load can jump instantly to a given value and stay at the same value for hours). If your source keeps its state for long periods of time, this method is preferable to the
right method follows the Right rule. The method is similar to the left method, but overestimates the intrinsic source. Again it is only appropriate to be used with rectangular functions.
An integration sensor is quite useful in energy billing scenarios since energy is generally billed in kWh and many sensors provide power in W (Watts).
If you have a sensor that provides you with power readings in Watts (uses W as
power), then you can use the
integration sensor to track how much energy is being spent. Take the next manual YAML configuration as an example:
- platform: integration
This configuration will provide you with
sensor.energy_spent which will have your energy in kWh, as a