Read this in other language: French
The electronic part of this weather station is based on the model described in an article in Elektor magazine published in May 2020 entitled Remake Elektor weather station (an evolution of ESP32 Weather Station described in an article in the same magazine in January 2019).
The article details the creation of a weather station based on a set of sensors referenced WH-SP-WS02 (Datasheet) whose original electronics are removed to be replaced by a sensor daughter board relaying the information to a motherboard built around an ESP32 (ESP32 Pico Kit).
An OpenSource firmware GitHub - ElektorLabs/191148-RemakeWeatherStation: RemakeWeatherStation is available to run the whole system. Unfortunately, I did not find my happiness with it, it is not yet complete enough and suffers from some shortcomings that make it very difficult to use it as is.
I therefore decided to use ESPHome as a replacement for the original program in order to simplify the development of functionality but above all to greatly extend its capabilities.
The board detailed in Elektor's article is finally limited to a voltage converter and a 5V/3V voltage adaptation for the ESP32.
It is therefore quite simple to recreate this weather station independently of Elektor's PCB. For the connections, please use the data included in the YAML file.
With the kind permission of Elektor, the schematic diagrams are available here: Schematics.
Note: On the main picture of the weather station, there is a box on top, it is an independent rain detection module.
In order to install the firmware on the ESP32, I invite you to follow the procedure described on the ESPHome website: Getting Started with ESPHome
The Elektor electronic board is to be used as is or, in view of its simplicity, to be reproduced on a test board.
The Elektor publication is wrong, in fact, they use GPIO 34 (wind speed) and 38 (precipitation measurement) directly without a pullup resistor, Moreover, these inputs / outputs do not integrate a pull-up resistor, so it is necessary to add a pullup resistor (~10kOhms) on GPIO34 and GPIO38.
For the mast, I used a metal reinforced PVC tube that you can find in any DIY store in the plumbing department. In order to fix it on a wall, I modeled it on OpenSCAD one piece that I then printed in PETG.
Here is the printed piece next to the box containing the solar panel charger controller:
One of the flaws that I reproach to the original Elektor board is to have linked the technology of the power source to the motherboard (in this case the lead battery), indeed, a MAX8212 used with some peripheral components allows to cut the power supply when it goes below a threshold defined by the value of 3 resistors. This threshold has been chosen to protect a lead battery.
Since a weather station is supposed to stay on all the time, I don't really understand the above choice because:
In the 2 cases mentioned above, a strong link is inserted on the motherboard with the battery technology, which should be done, imho, on an independent card / module.
For my part, I chose to power my weather station via a 30W solar panel and a relatively basic charge controller.
If you are using Elektor's motherboard with an independent load controller module, do not forget to lower the cut-off threshold of the MAX8212.
At the output of the solar panel, the INA219 circuit allows the power generated by the solar panel to be measured. I plan to replace it with INA3221 in order to also measure the total consumption of the weather station and help me to refine the global consumption (a RFLink with OpenMQTTGateway and a second ESP32 kit is connected to the station).
I use a 7A lead battery recovered from an old inverter.
To size the whole thing, I recommend the BatteryStuff Tools tool which is very handy.
Currently, I can't say that I have succeeded in making my weather station totally energy independent because of a bad exposure of my solar panel and a too high consumption of an additional module (rain detection module).
These 3 quantities are measured by a Bosch BME280 sensor and its configuration in ESPHome is as follows:
- platform: bme280
address: 0x76
update_interval: 60s
iir_filter: 16x
temperature:
name: "${friendly_name} temperature"
oversampling: 16x
humidity:
name: "${friendly_name} humidity"
oversampling: 16x
pressure:
name: "${friendly_name} pressure"
oversampling: 16x
The sensor is to be put inside the box containing the original sensor electronics.
Initially, I also included an AM2320 sensor to compare the sensor values with the following configuration:
- platform: am2320
setup_priority: -100
temperature:
id: am2320_temperature
name: "${friendly_name} AM2320 temperature"
humidity:
id: am2320_humidity
name: "${friendly_name} AM2320 humidity"
update_interval: 60s
The temperatures and humidities of the sensors were averaged before being sent to Home Assistant (see below), it was of course possible to access the data of each sensor.
- platform: template
name: "${friendly_name} temperature"
icon: "mdi:thermometer"
unit_of_measurement: "°C"
lambda: |-
return (
id(bme280_temperature).state
+
id(am2320_temperature).state
) / 2;
- platform: template
name: "${friendly_name} humidity"
icon: "mdi:water-percent"
unit_of_measurement: "%"
lambda: |-
return (
id(bme280_humidity).state
+
id(am2320_humidity).state
) / 2;
At the end of my tests, the BME280 sensor is more reliable and more accurate than the AM2320 sensor.
The wind speed sensor is connected to general input 34 and the ESPHome pulse_meter
platform is now used to perform the measurement.
You need to add a pulling resistor on the GPIO34 (there is no internal pullup on this GPIO).
$number_of_pulses_by_revolution
)circumference_in_meter = $radius * 2 * π
circumference_in_meter = 0.09 * 2 * 3.14
circumference_in_meter = 0.565486678
rotations_per_sec = pulses / $number_of_pulses_by_revolution / 60
meter_per_second = 1.18 * circumference_in_meter * $rotations_per_sec
meter_per_second = 1.18 * circumference_in_meter * 1 / $number_of_pulses_by_revolution / 60
meter_per_second = 1.18 * 0.565486678 / 2 / 60
meter_per_second = 0.005560619
Initial formula from https://github.com/mkuoppa/esphomeweatherstation/issues/2#issuecomment-812686624
For more information about calculation, see https://github.com/hugokernel/esphome-weather-station/issues/6
- platform: pulse_meter
pin:
number: GPIO34
mode: INPUT
id: wind_speed
unit_of_measurement: 'm/s'
name: "${friendly_name} wind speed"
icon: 'mdi:weather-windy'
internal_filter: 13us
timeout: 5s
filters:
- multiply: 0.005560619
- sliding_window_moving_average:
window_size: 5
send_every: 5
The wind direction is made in the sensor by means of magnets (switch reed) that switch resistors. Depending on the final value, the direction is deduced.
An example of the ESPHome configuration:
- platform: resistance
sensor: source_sensor
id: resistance_sensor
configuration: DOWNSTREAM
resistor: 10kOhm
internal: true
name: Resistance Sensor
reference_voltage: 3.9V
accuracy_decimals: 1
filters:
- median:
window_size: 7
send_every: 4
send_first_at: 3
on_value:
- if:
condition:
sensor.in_range:
id: resistance_sensor
above: 15000
below: 15500
then:
- text_sensor.template.publish:
id: wind_dir_card
state: "N"
- sensor.template.publish:
id: wind_heading
state: 0.0
[...]
The measurement of precipitation is carried out by a system of pendulum composed of 2 cups, the water runs in the funnel of the sensor and fills the high cup, once the latter is filled, it tips by gravity. This movement is detected by a magnetic sensor (reed switch) and an impulse is generated. The sensor documentation indicates that each pulse corresponds to 0.2794mm of precipitation.
- platform: pulse_counter
pin:
# Don't forget to add a pulling resistor, see README
number: GPIO38
mode: INPUT
unit_of_measurement: 'mm'
name: "${friendly_name} rain gauge"
icon: 'mdi:weather-rainy'
id: rain_gauge
internal: true
count_mode:
rising_edge: DISABLE
falling_edge: INCREMENT
internal_filter: 13us
update_interval: 60s
filters:
# Each 0.011" (0.2794mm) of rain causes one momentary contact closure
- multiply: 0.2794
accuracy_decimals: 4
In order to have more relevant information, these measurements are converted into precipitation per minute and the daily total is calculated.
- platform: integration
name: "${friendly_name} rainfall per min"
id: rain_per_min
time_unit: min
unit_of_measurement: 'mm'
icon: 'mdi:weather-rainy'
sensor: rain_gauge
- platform: total_daily_energy
name: "${friendly_name} total daily rain"
power_id: rain_gauge
unit_of_measurement: 'mm'
icon: 'mdi:weather-rainy'
# x60 To convert to aggregated rain amount
filters:
- multiply: 60
Remember to position the brightness sensor as high as possible on your weather station so that it is not shaded by the mast or any part of the weather station.
- platform: tsl2561
id: lux_meter
name: "${friendly_name} ambient Light"
address: 0x39
update_interval: 5s
integration_time: 14ms
gain: 1x