Real Time Weather Prediction System Using IOT and Machine Learning

Abstract

Today’s the focus has been shifted towards intelligent technologies like IoT and Machine Learning. Many IoT hardware platforms are available for IoT implementations. ESP8266 chip is one of them. This paper implements the real time weather prediction system that can be used in number of applications like homes, industries, agriculture, stadiums etc. for predicting the weather information. The system utilizes a temperature and humidity sensor i.e. DHT11 and a light intensity sensor i.e. LDR. The sensed data from the sensors are uploaded to a ThingSpeak cloud server using NodeMCU and ESP8266-01 module. The data is also displayed on a customized HTML webpage for monitoring the real time values. A logistic regression model is used for setting up the machine learning environment. This model is trained using the pre- recorded values of sensor data. Further, NodeMCU records the data from sensors i.e. temperature, humidity and light intensity and then the values are transferred to the Jupyter notebook that utilizes a python environment. This real time data is used to test the model and prediction is done for a particular value by blinking the led connected to NodeMCU.