Makers and hobbyists looking for a project to keep them busy this weekend may be interested in a new Raspberry Pi DIY weather station. Built using a little Arduino and Raspberry Pi hardware the weather station features a Arduino Nano 33 BLE and Raspberry Pi 4 Model B together with a DFRobot I2C Ozone Sensor.
The anemometer used to capture windspeed is available from the DFRobot website priced at $48 and calculates wind speed level through voltage signal output from 0 to 5 volts. The project has been created by Kutluhan Aktar and although the project is quite advanced it might provide inspiration on how to build your very own simpler Arduino windspeed monitor and weather station.
DIY Raspberry Pi weather station
“Ever want to build your own weather station? This anemometer is made of shell, wind cups and circuit module. It has built-in photovoltaic module, industrial microcomputer processor, and standard current generator. Made with aluminium alloy, the anemomter is of high strength, weather resistance and corrosion resistance. The millitary quality interface ensures long life of this anemometer, at the same time enhancing the accuracy of wind speed acquisition.”
“Since the risk of breathing polluted air with poor quality has been increased precipitously in recent decades, measuring air quality to take precautions so as to keep our respiratory system healthy is crucial, especially for sensitive groups, which include people with lung disease such as asthma, older adults, children, teenagers, and people who are active outdoors. Even though there are international efforts to reduce air pollution and deplete air pollutants, tracking air quality locally to get prescient warnings regarding pulmonary risk factors is yet a pressing issue.”
To learn more about the Raspberry Pi weather station jump over to the Hackster.io project page by following the link below where a full list of all the components you will need. Together with full instructions to help you build your very own home-based weather station to check the local weather in your area. As well as build and train a TensorFlow neural network model, and run the model to predict air quality.