Data fetcher for Liquidcheck waterlevel sensor and open-meteo weather data 🚰☁️📂

A data fetching tool combining the waterlevel readings from a Liquid Check waterlevel sensor (by SI-Elektronik) and weather data provided by open-meteo.

Background 📖

A waterlevel sensor is used to monitor the water level of rainwater collection tank. The determining factor of the demand is the use case (predominantly irrigation). The supply is determined by the amount of rainwater collected. Both are expected to show correlations to meteorological data. To analyze (and hopefully predict) waterlevels, relevant data needs to be gathered. A collection of meteorological data was selected (see here).

Usage

Configuration ⚙️

.env 📄

All config parameters can be changed in the .env file.
For the open.meteo API-call the latitude, longitude and timezone need to be set. The given timezone is also used to set the timezone of the container itself. The url-endpoint of the sensor needs to be set accordingly. See manufacturers documentation for that.
The schedule for running the script is set using a cron job. Define the planned interval using CRON_INTERVAL in the crontab-format.
CSV_PATH is used to bind a host directory for data storage. If an influxdb instance is used additonally, define the corresponding values in .env file.

#openmeteo params
LATITUDE=XX.XXXX
LONGITUDE=XX.XXXX
TIMEZONE="XYZ/XYZ"

#Liquid Check params
URL_ENDPOINT="XYZ" #(default: "http://liquid-check/infos.json")

#docker params
#docker-compose uses TIMEZONE for TZ of container

#cron scheduling (e.g. hourly)
CRON_INTERVAL=0 * * * *
#bind host directory for data-output
CSV_PATH=/PATH/on/host/data

#if influxdb is used
INFLUXDB_TOKEN ="yxz123"
INFLUXDB_BUCKET = "bucket-name"
ORG = "org-name"
URL = "http://xyz:1234"

Spinning up a container 🚢

Create container using docker compose and run it in the background

docker compose up -d

The data fetcher now runs periodically and writes gathered data into a csv-file.

Result

The csv-ouput stores the values as following:

Parameter	Range	Information
Water level
timestamp	current	%Y-%m-%d %H:%M:%S
wl_level	current	Water level in m
wl_content	current	Content in l
wl_percent	current	Level of filling in %
wl_age	current	Time elapsed since last measurement in s
open-meteo weather data		See open-meteo documentation
owm_d_temperature_2m_max	-4 to +5	Maximum daily air temperature at 2 meters above ground
owm_d_temperature_2m_min	-4 to +5	Minimum daily air temperature at 2 meters above ground
owm_d_sunshine_duration	-4 to +5	The number of seconds of sunshine per day is determined by calculating direct normalized irradiance exceeding 120 W/m², following the WMO definition. Sunshine duration will consistently be less than daylight duration due to dawn and dusk.
owm_d_uv_index_max	-4 to +5	Daily maximum in UV Index starting from 0.
owm_d_precipitation_sum	-4 to +5	Sum of daily precipitation (including rain, showers and snowfall)
owm_d_rain_sum	-4 to +5	Sum of daily rain
owm_h_soil_moisture_3_to_9cm	-4 to +5	Average soil water content as volumetric mixing ratio at 3-9 cm depth in m3. Daily means are calculated.
owm_h_soil_moisture_9_to_27cm	-4 to +5	Average soil water content as volumetric mixing ratio at 9-27 cm depth in m3. Daily means are calculated.
owm_h_soil_moisture_27_to_82cm	-4 to +5	Average soil water content as volumetric mixing ratio at 27-81 cm depth in m3. Daily means are calculated.
owm_c_current_temperature	current	°C
owm_c_current_relative_humidity_2m	current	%
owm_c_current_rain	current	mm
owm_c_current_surface_pressure	current	hPa

Range: The calculated timedelta in days between current time and gathered meteo data. All parameters are stored for each day (-4 days back and up to +5 days of forecast).

Appendix

open-meteo data 📋

The following selection of parameters is used for the api-call to open-meteo.
See details in the openmeteo_apicall.py

params ={
	"latitude": os.environ["LATITUDE"],
	"longitude": os.environ["LONGITUDE"],
	"current": ["temperature_2m", "relative_humidity_2m", "precipitation", "rain","surface_pressure"],
	"hourly": ["soil_moisture_3_to_9cm", "soil_moisture_9_to_27cm", "soil_moisture_27_to_81cm"],
	"daily": ["temperature_2m_max", "temperature_2m_min", "sunshine_duration", "uv_index_max", "precipitation_sum", "rain_sum"],
	"timezone": os.environ.get("TIMEZONE", "Europe/Berlin"),
	"past_days": 3
}

The selection can be easily adapted. Changes to the datafechter_wlowm.py and the data processing are necessary.

Outlook 🔭

• supporting storing data into InfluxDB instance
  

• training model for waterlevel prediction