Home Assistant Automations

To automate EMHASS with Home Assistant, we will need to define some shell commands in the Home Assistant configuration.yaml file and some basic automations in the automations.yaml file. In the next few paragraphs, we are going to consider the dayahead-optim optimization strategy, which is also the first that was implemented, and we will also cover how to publish the optimization results.
Additional optimization strategies were developed later, that can be used in combination with/replace the dayahead-optim strategy, such as MPC, or to expand the functionalities such as the Machine Learning method to predict your household consumption. Each of them has some specificities and features and will be considered in dedicated sections.

Dayahead Optimization - Method 1) Add-on and docker standalone

We can use the shell_command integration in configuration.yaml:

shell_command:
  dayahead_optim: "curl -i -H \"Content-Type:application/json\" -X POST -d '{}' http://localhost:5000/action/dayahead-optim"
  publish_data: "curl -i -H \"Content-Type:application/json\" -X POST -d '{}' http://localhost:5000/action/publish-data"

An alternative that will be useful when passing data at runtime (see dedicated section), we can use the the rest_command instead:

rest_command:
  url: http://127.0.0.1:5000/action/dayahead-optim
  method: POST
  headers:
    content-type: application/json
  payload: >-
    {}

Dayahead Optimization - Method 2) Legacy method using a Python virtual environment

In configuration.yaml:

shell_command:
  dayahead_optim: ~/emhass/scripts/dayahead_optim.sh
  publish_data: ~/emhass/scripts/publish_data.sh

Create the file dayahead_optim.sh with the following content:

#!/bin/bash
. ~/emhassenv/bin/activate
emhass --action 'dayahead-optim' --config ~/emhass/config.json

And the file publish_data.sh with the following content:

#!/bin/bash
. ~/emhassenv/bin/activate
emhass --action 'publish-data' --config ~/emhass/config.json

Then specify user rights and make the files executables:

sudo chmod -R 755 ~/emhass/scripts/dayahead_optim.sh
sudo chmod -R 755 ~/emhass/scripts/publish_data.sh
sudo chmod +x ~/emhass/scripts/dayahead_optim.sh
sudo chmod +x ~/emhass/scripts/publish_data.sh

Common for any installation method

Options 1, Home Assistant automate publish

In automations.yaml:

- alias: EMHASS day-ahead optimization
  trigger:
    platform: time
    at: '05:30:00'
  action:
  - service: shell_command.dayahead_optim
- alias: EMHASS publish data
  trigger:
  - minutes: /5
    platform: time_pattern
  action:
  - service: shell_command.publish_data

In these automations the day-ahead optimization is performed once a day, every day at 5:30am, and the data (output of automation) is published every 5 minutes.

Option 2, EMHASS automated publish

In automations.yaml:

- alias: EMHASS day-ahead optimization
  trigger:
    platform: time
    at: '05:30:00'
  action:
  - service: shell_command.dayahead_optim
  - service: shell_command.publish_data

in configuration page/config.json

"method_ts_round": "first"
"continual_publish": true

In this automation, the day-ahead optimization is performed once a day, every day at 5:30am. If the optimization_time_step parameter is set to 30 (default) in the configuration, the results of the day-ahead optimization will generate 48 values (for each entity), a value for every 30 minutes in a day (i.e. 24 hrs x 2).

Setting the parameter continual_publish to true in the configuration page will allow EMHASS to store the optimization results as entities/sensors into separate json files. continual_publish will periodically (every optimization_time_step amount of minutes) run a publish, and publish the optimization results of each generated entities/sensors to Home Assistant. The current state of the sensor/entity being updated every time publish runs, selecting one of the 48 stored values, by comparing the stored values’ timestamps, the current timestamp and 'method_ts_round': "first" to select the optimal stored value for the current state.

option 1 and 2 are very similar, however, option 2 (continual_publish) will require a CPU thread to constantly be run inside of EMHASS, lowering efficiency. The reason why you may pick one over the other is explained in more detail below in continual_publish.

Lastly, we can link an EMHASS published entity/sensor’s current state to a Home Assistant entity on/off switch, controlling a desired controllable load. For example, imagine that I want to control my water heater. I can use a published deferrable EMHASS entity to control my water heater’s desired behavior. In this case, we could use an automation like the below, to control the desired water heater on and off:

on:

automation:
- alias: Water Heater Optimized ON
  trigger:
  - minutes: /5
    platform: time_pattern
  condition:
  - condition: numeric_state
    entity_id: sensor.p_deferrable0
    above: 0.1
  action:
    - service: homeassistant.turn_on
      entity_id: switch.water_heater_switch

off:

automation:
- alias: Water Heater Optimized OFF
  trigger:
  - minutes: /5
    platform: time_pattern
  condition:
  - condition: numeric_state
    entity_id: sensor.p_deferrable0
    below: 0.1
  action:
    - service: homeassistant.turn_off
      entity_id: switch.water_heater_switch

These automations will turn on and off the Home Assistant entity switch.water_heater_switch using the current state from the EMHASS entity sensor.p_deferrable0. sensor.p_deferrable0 being the entity generated from the EMHASS day-ahead optimization and published by examples above. The sensor.p_deferrable0 entity’s current state is updated every 30 minutes (or optimization_time_step minutes) via an automated publish option 1 or 2. (selecting one of the 48 stored data values)